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Abstract Aims To assess the stage of acute kidney injury (AKI), as an index of organ perfusion, combined with shock severity, measured by the Society for Cardiovascular Angiography and Interventions (SCAI) shock stage classification, to stratify the risk of mortality in patients diagnosed with cardiogenic shock (CS) and supported with venoarterial extracorporeal membrane oxygenation (VA ECMO). Methods ans results From January 2018 to December 2020, consecutive adult patients diagnosed with CS and received VA ECMO were retrospectively evaluated. The highest AKI stage within 48 h after ECMO initiation was assessed using the Kidney Disease: Improving Global Outcomes criteria. We included 216 patients with a mean age of 58.8 years and 31.0% were females. 88.4% of patients received ECMO for postcardiotomy, while 11.6% for medical CS. The total in‐hospital mortality was 53.2%. AKI occurred in 182 (84.3%) patients receiving ECMO for CS. AKI stage 0, 1, 2, and 3 were present in 15.7%, 17.6%, 18.1%, and 48.6% of patients with in‐hospital mortality of 26.5%, 26.3%, 61.5%, and 68.6%, respectively (P

The situation of coal as the stabilizer guarantee the energy safety in China cannot be changed in the short term, and it will continue to face the problems of coal resource protection and the low utilization rate of coal-based solid waste. Through innovative mining methods, improving the recovery rate of coal resources and the large-scale disposal of coal-based solid waste, a new path is being paved for the green and sustainable development of the coal industry. Based on the in-depth research on the development status of coal mining technology, a new roadway excavation method of “excavation-backfill-retention” is proposed. This paper systematically discusses the scientific problems and key technologies of the “excavation-backfill-retention” construction method from three aspects: rapid excavation, continuous and efficient backfilling and safe construction of large-section roadways. The research results show that ① aiming at the engineering problems such as the fast excavation of large section roadway and the stability control of surrounding rock of heading face, three scientific problems are summarized: the matching mechanism between coal and rock characteristics and cutting parameters of roadway boring machine, the spatial transfer mechanism of overburden load and the deformation mechanism of surrounding rock, the interaction relationship between anchor/cable roof and support mechanism. The technology system has been built, which is based on the real-time sensing of the geological environment in the excavation area, the intelligent cooperative operation of the multi-process of falling, loading and transporting coal, the aging control of surrounding rock of the drilling anchor support together with excavation, the anti-leakage and ventilation optimization of the large section roadway. ② The scientific problems of continuous and efficient backfilling are summarized from the two aspects of timely construction of large section roadway bearing system and its bearing performance regulation, including the co-bearing mechanism of background coal anchor/cable, the hydration consolidation mechanism of multi-component solid waste base backfill materials, and the space-time evolution law of physical and mechanical characteristics of backfill are clarified. A continuous and efficient backfill key technology system with the core of safe and efficient backfill space construction, backfill material performance control and backfill material flow-consolidation perception has been established. The breakthrough of relevant theories and technologies can provide a theoretical basis for raw material selection and ratio design of backfill materials, additive development/selection and working performance control, excavation rate and backfill step optimization design, consolidation monitoring of backfill body, etc. ③ Based on the key problems of roadway safety and surrounding rock easy to break, sheet and damage in the whole life cycle, the paper elaborates three scientific problems: the spatial and temporal distribution characteristics of stress field of roadway surrounding rock, the evolution mechanism of roadway deformation and damage, the engineering quality monitoring and stability regulation theory of roadway surrounding rock. The key technology system is formed with the intelligent stability warning of the rock surrounding the roadway and the deformation control of the rock surrounding the roadway as the core. It is of great significance to conduct the scientific research and technical demonstration of the “excavation-backfill-retention” construction method in coal mines, and to realize the synergy of large-scale solid waste disposal, efficient coal pillar section recovery and rapid roadway excavation, which can promote the green and low-carbon transformation and development of the coal industry.

Accurately predicting the spatio-temporal evolution trends and long-term dynamics of three-dimensional ocean temperature and salinity plays a crucial role in monitoring climate system changes and conducting fundamental oceanographic research. Numerical models are the most prevalent of the traditional approaches, which are often too complex and lack of generality. Recently, with the rise of AI, many data-driven methods are proposed. However, most of them take no consideration of natural physical laws that may cause issues of physical inconsistency among different variables. In this paper, we proposed PGTransNet, a novel physics-guided transformer network for 3D Ocean temperature and salinity forecasting. This model is based on Vision Transformer, and to enhance the performance we have three aspects of improvements. Firstly, we design a loss function that deliveries the physical relationship among temperature, salinity and density by fusing the Thermodynamic Equation. Secondly, to capture global and long-term dependencies effectively, we add the Pacific Decadal Oscillation (PDO) and North Pacific Gyre Oscillation (NPGO) in the embedding layer. Thirdly, we adopted the Laplacian sparse positional encodings to alleviate the artifacts caused by high-norm tokens. The former two are the core components to leverage the physical information. Finally, to comprehensively evaluate PGTransnet, we conduct rich experiments in metrics RMSE, Anomoly Correlation Coefficients, Bias and physical consistency. Our proposal demonstrates higher prediction accuracy with fast convergence, and the metrics and visualizations show that our model is insensitive to hyperparameter tuning, ensuring better generalization and adherence to physical consistency. Moreover, as observed from the spatial distribution of the anomaly correlation coefficient, the model exhibits higher forecasting accuracy for coastal and marginal sea regions.

With the proliferation of electric vehicles (EVs) around the world, the large-scale grid-vehicle interaction (GVI) has become more and more promising recently. However, the existing research works on GVI are mainly limited to day-ahead or intra-day schedules which may not fully utilize the EVs' potential in charging/discharging arrangements for the power grid. Besides, the commonly-adopted practice of the central organization by the electric vehicle aggregator (EVA) may induce EV owners' privacy concerns. To solve these problems and furtherly strengthen the GVI willingness of EV users, a distributed week-ahead framework for the charging / discharging schedules of plug-in EVs interacting with the grid under the organization of an EVA is proposed in this paper. To describe the available dispatchable capacity of one single EV on a weekly time scale, the trip-chain based EV charging and travelling behavior model is formulated. For the EVA, it utilizes aggregate EV power and energy boundaries to model its feasible operation region which is crucial for the determination of the guidance tracking power profile in the distribution energy market clearing process. To protect the EVs' privacies and reduce the computational burden, a distributed algorithm is proposed to help the EVA allocate the guidance power profile among EVs. Case studies are conducted to demonstrate the validness and effectiveness of the proposed methods.

Background: This study used machine learning to categorize cardiogenic shock (CS) patients treated with venous–arterial extracorporeal membrane oxygenation (VA-ECMO) into distinct phenotypes. Subsequently, it aimed to clarify the wide mortality variance observed in refractory CS, attributing it to the condition’s inherent heterogeneity. Methods: This study enrolled a cohort of CS patients who received VA-ECMO support. By employing rigorous machine learning (ML) techniques, we generated and validated clusters based on determinants identified through algorithmic analysis. These clusters, characterized by distinct clinical outcomes, facilitated the examination of clinical and laboratory profiles to enhance the understanding of patient responses to VA-ECMO treatment. Results: In a study of 210 CS patients undergoing VA-ECMO treatment, 70.5% were male with a median age of 62, ranging from 53 to 67 years. Survival rates were 67.6% during VA-ECMO and 49.5% post-discharge. Patients were classified into three phenotypes based on the clinical and laboratory findings: “platelet preserved (I)”, those with stable platelet counts, “hyperinflammatory (II)”, those indicating significant inflammation, and “hepatic–renal (III)”, those showing compromised liver and kidney functions. Mortality rates (25.0%, 52.8%, and 55.9% for phenotypes I, Ⅱ, and Ⅲ, respectively (p = 0.005)) varied significantly among these groups, highlighting the importance of phenotype identification in patient management. Conclusions: This study identified three distinct phenotypes among refractory CS patients treated using VA-ECMO, each with unique clinical characteristics and mortality risks. Thus, highlighting the importance of early detection and targeted intervention, these findings suggest that proactive management could improve outcomes for those showing critical signs.

Enhancing the path planning capabilities of ships is crucial for ensuring navigation safety, saving time, and reducing energy consumption in complex maritime environments. Traditional methods, reliant on static algorithms and singular models, are frequently limited by the physical constraints of ships, such as turning radius, and struggle to adapt to the maritime environment’s variability and emergencies. The development of reinforcement learning has introduced new methods and perspectives to path planning by addressing complex environments, achieving multi-objective optimization, and enhancing autonomous learning and adaptability, significantly improving the performance and application scope. In this study, we introduce a two-stage path planning approach for large ships named MAPF–DQN, combining Manipulation-Compliant Artificial Potential Field (MAPF) with Deep Q-Network (DQN). In the first stage, we improve the reward function in DQN by integrating the artificial potential field method and use a time-varying greedy algorithm to search for paths. In the second stage, we use the nonlinear Nomoto model for path smoothing to enhance maneuverability. To validate the performance and effectiveness of the algorithm, we conducted extensive experiments using the model of “Yupeng” ship. Case studies and experimental results demonstrate that the MAPF–DQN algorithm can find paths that closely match the actual trajectory under normal environmental conditions and U-shaped obstacles. In summary, the MAPF–DQN algorithm not only enhances the efficiency of path planning for large ships, but also finds relatively safe and maneuverable routes, which are of great significance for maritime activities.

Economic dispatch (ED) aims to minimize the generation cost subject to power balance constraints. It is extensively used in power system operation and planning. ED problem as well as other problems with the same formulation are named as ED-type problems in this letter and a fast solution method is provided. The proposed method is achieved by solving a series of relaxed problems. With a closed-form solution for the relaxed ED-type problems, it is demonstrated that the proposed method consumes far less computing time and memory space than the off-the-shelf solvers and other quadratic programming (QP) methods. Finally, the effectiveness and computational efficiency of the proposed method are verified by the case studies, which shows the great potential in power system planning and operation.

Background: The relationship between the magnitude of platelet count decrease and mortality in post-cardiotomy cardiogenic shock (PCS) patients undergoing venoarterial extracorporeal membrane oxygenation (VA-ECMO) has not been well-reported. This study was designed to evaluate the association between the relative decrease in platelet count (RelΔplatelet) at day 1 from VA-ECMO initiation and in-hospital mortality in PCS patients.Methods: Patients (n = 178) who received VA-ECMO for refractory PCS between January 2016 and December 2018 at the Beijing Anzhen Hospital were reviewed retrospectively. Multivariable logistic regression analyses were performed to assess the association between RelΔplatelet and in-hospital mortality.Results: One hundred and sixteen patients (65%) were weaned from VA-ECMO, and 84 patients (47%) survived to hospital discharge. The median [interquartile range (IQR)] time on VA-ECMO support was 5 (3–6) days. The median (IQR) RelΔ platelet was 41% (26–59%). Patients with a RelΔ platelet ≥ 50% had an increased mortality compared to those with a RelΔ platelet < 50% (57 vs. 37%; p < 0.001). A large RelΔplatelet (≥50%) was independently associated with in-hospital mortality after controlling for potential confounders (OR 8.93; 95% CI 4.22–18.89; p < 0.001). The area under the receiver operating characteristic curve for RelΔ platelet was 0.78 (95% CI, 0.71–0.85), which was better than that of platelet count at day 1 (0.69; 95% CI, 0.61–0.77).Conclusions: In patients receiving VA-ECMO for post-cardiotomy cardiogenic shock, a large relative decrease in platelet count in the first day after ECMO initiation is independently associated with an increased in-hospital mortality.

The smart grid with flexible topologies receives intensive attention recently. Transmission switching (TS) alters the power system topology during operation, and has been demonstrated for the advantage of economic and secure operation of power systems. TS includes a chain of sequential switching actions which bring disturbances to the system if the switching actions are not properly designed. Unfortunately, it is not considered or well-studied in existing works. In this paper, a new multi-period TS model that considers the transition security and a two-stage iterative method are proposed. In the TS model, we take into account the fact that only one line is permitted to switch up or down at a time and the security of each switching action is considered. The proposed iterative solution makes the TS model more tractable under AC framework. Case studies on a 6-bus system and the IEEE 57-bus test system have varified the effectiveness of the proposed model. Numerical results show that: ➀ the consideration of transition security of TS is essential; ➁ the transition path is directly related to secure and fast the transmission switching; ➂ the proposed model and solution method give an effective way to determine the switching sequence and switching timing under transition security criteria.

In modern distribution system, the distribution system operator (DSO) acts as a market facilitator and data manager as well as an energy supplier and operation controller. In this circumstance, the DSO should comprehensively consider the diversified participants of the modern distribution system when making investment decisions of distributed generation (DG). This paper proposes a DG planning model considering the behavior of the diversified participants, which are motivated to cooperate with distributed renewable energy resources to promote their integration, and to achieve the optimal DG investment plan. The optimization model takes a centralized structure but fully considers the preferences, profits and comfort levels of the aggregators and consumers. The model is linearized into a mixed-integer linear programming (MILP) problem and is solved by CPLEX. Results of the case study show that when the DSO spares subsidies to the aggregators and consumers to encourage their participation in demand response (DR) programs, it earns more compared with providing no subsidies for DR participation. It is also demonstrated that the overall profit increases as the subsidies increase within a certain range, but decreases when the subsidies exceed this range. Therefore, the DSO needs to carefully choose the subsidization level to achieve the optimal utilization of renewable energy and demand flexibility. The optimal subsidization level is derived from the model proposed in this paper. Therefore, this paper puts forward a new pattern to utilize the distributed renewable energy sources, and provides guidance in policy making and DR program implementation.

Online misinformation is a threat to a well-informed electorate and undermines democracy. Here, the authors analyse the spread of articles on Twitter, find that bots play a major role in the spread of low-credibility content and suggest control measures for limiting the spread of misinformation.

Wind power generation has been developing rapidly in recent years. Therefore, the mechanism for promoting wind power integration becomes increasingly important. In this paper, a novel mechanism of intraday market is proposed, which can utilize updating information of wind power and effectively exploit the shiftable load. The mechanism is compatible with the existing market framework, and promotes wind power producers (WPPs) to be more competitive in the market. Thus, the WPPs could make decisions for multiple market transactions in various trading timescales according to the market price. Moreover, the WPPs can optimize their decisions continuously in the intraday market at different time points in the model. The case study on IEEE-RTS verifies the proposed method is valid and feasible. It can support wind power integration and increase the benefit of the whole system as well.

Massive amounts of misinformation have been spreading over social media during the 2016 U.S. election season, causing wide public concern about our information ecosystem. How does fact checking compete with misinformation for user attentions? How do misinformation and fact checking differ in their spreading patterns? And what strategies are used by social bots to promote the spread of misinformation? In this paper, we address these research questions by analyzing datasets collected by Hoaxy. We answer the first question by conducting: 1) a survival analysis shows that about 70% of claims will be fact-checked in one week and about 800 tweets with claim links are posted during this period, on average and 2) a cross-correlation analysis shows that the sharing of fact-checking articles typically lags that of misinformation by about one day. Regarding the second question, we answer it from three points of view: 1) we find that false claims can become more popular than the corresponding debunking; 2) when looking at the distribution of types of tweets, we find that fact checking tends to spread in a conversational way; and 3) bot behavior analysis shows that the most active accounts sharing misinformation behave more like social bots. To better understand how social bots spread misinformation, we conduct case studies to answer the third question. By presenting strategies such as the production of a large number of original tweets, the alternating and hijacking of hashtags, and the injection of content into conversations, we demonstrate how social bots take advantages of the recommendation features of Twitter to amplify the spread of misinformation.

Massive amounts of fake news and conspiratorial content have spread over social media before and after the 2016 US Presidential Elections despite intense fact-checking efforts. How do the spread of misinformation and fact-checking compete? What are the structural and dynamic characteristics of the core of the misinformation diffusion network, and who are its main purveyors? How to reduce the overall amount of misinformation? To explore these questions we built Hoaxy, an open platform that enables large-scale, systematic studies of how misinformation and fact-checking spread and compete on Twitter. Hoaxy captures public tweets that include links to articles from low-credibility and fact-checking sources. We perform k-core decomposition on a diffusion network obtained from two million retweets produced by several hundred thousand accounts over the six months before the election. As we move from the periphery to the core of the network, fact-checking nearly disappears, while social bots proliferate. The number of users in the main core reaches equilibrium around the time of the election, with limited churn and increasingly dense connections. We conclude by quantifying how effectively the network can be disrupted by penalizing the most central nodes. These findings provide a first look at the anatomy of a massive online misinformation diffusion network.