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An Inconel 740H nickel-based alloy was fabricated via wire arc additive manufacturing. The as-welded and heat-treated samples were analyzed to investigate their phase composition, microstructure, crystal structure, and mechanical properties. After heat treatment, the sample exhibited a columnar crystal zone microstructure consisting of a γ matrix + precipitated phase, the remelting zone metallographic structure was a γ matrix + precipitated phase, and the HAZ metallographic structure was a γ matrix + precipitated phase. Transmission electron microscopy (TEM) and electron backscatter diffraction (EBSD) were used to show that the welded sample exhibited many dislocations, a few inclusions, and carbides, nitrides, and γ’ precipitates in its crystal structure. In contrast, the crystal structure of the heat-treated sample exhibited a lower number of dislocations and significantly higher carbide and γ’ precipitate content. Moreover, the mechanical performance of these samples was excellent. This heat-treatment process improved the sample strength by about 200 MPa, leading to better high-temperature mechanical properties. This work is anticipated to offer theoretical and experimental support for using additive manufacturing methods in the manufacturing of nickel-based superalloy components.

Background Patients with left ventricular thrombus (LVT) resolution can have LVT recurrence and risk for thromboembolism. However, these outcomes after LVT resolution are not well known. We aimed to assess the prevalence, risk factors, and clinical outcomes for LVT recurrence in patients with LVT resolution to inform follow‐up and treatment. Methods and Results Patients with LVT resolution were identified retrospectively from a large echocardiography database between January 2009 and May 2022. Participants had echocardiograms at 3 time points, including baseline at LVT diagnosis, at LVT resolution, and a follow‐up for identification of LVT recurrence. The cumulative LVT recurrence rate was estimated by the Kaplan–Meier method, and predictors of LVT recurrence were evaluated using Cox regression analysis. Among 115 patients with LVT resolution, 28 (24.3%) had LVT recurrence at a median follow‐up of 1.2 (0.5–2.8) years. LV aneurysm (hazard ratio [HR], 2.59 [95% CI, 1.20–5.58], P=0.015) and anticoagulant use (HR, 0.12 [95% CI, 0.04–0.41], P=0.001) were predictors of LVT recurrence on multivariable analysis. Patients with an LV aneurysm who did not receive any anticoagulation demonstrated an LVT recurrence rate of 69.5%, whereas those without an LV aneurysm who received anticoagulation had a recurrence rate of 0%. Patients with LVT recurrence had a higher incidence of an embolic event (10.7% versus 1.1%, P=0.016). Conclusions LVT recurrence after LVT resolution is common, especially in those with an LV aneurysm, and is associated with a higher embolic risk. Continued anticoagulation is protective against LVT recurrence, although bleeding risk needs to be considered. These findings can inform follow‐up and treatment of patients with documented LVT resolution.

BackgroundHyperlactatemia is a prognostic marker among patients with ST-segment elevation acute myocardial infarction (STEMI). However, the predictive value of lactate and the dynamic change associated with acute kidney injury (AKI) among patients with STEMI, remain poorly understood. We aimed to compare single lactate values at admission (Lacadm) and 12 h after admission (Lac12h) with lactate clearance (LC) 12 h after admission for AKI prediction in patients with STEMI.MethodsA total of 1,784 patients with STEMI were included. The study endpoint was AKI occurrence during hospitalization. The predictive value of lactate levels measured at admission and 12 h after admission and LC for AKI prediction was determined using multivariate logistic regression analyses and compared with receiver operator characteristic (ROC) curve analysis.ResultsOverall, AKI was observed in 353 (19.8%) patients. In multivariate logistic regression analyses, Lacadm ≥ 4.3 mmol/L (OR: 1.53; 95% CI: 1.01–2.30), Lac12h ≥ 2.1 mmol/L (OR: 1.81; 95% CI: 1.36–2.42), and LC ≥ −7.5% (OR: 0.40; 95% CI: 0.30–0.53) were the independent predictive factors for AKI after adjusting for confounders. ROC curve analysis results revealed that Lac12h (0.639; 95% CI: 0.616–0.661) exhibited a significantly higher area under the curve (AUC) than those of Lacadm (0.551; 95% CI: 0.527–0.574) and LC (0.593; 95% CI: 0.570–0.616) in the prediction of AKI. LC (△AUC = 0.037, p < 0.001) and Lac12h (△AUC = 0.017, p = 0.029) enhanced the discrimination capacity of Mehran Risk Score (MRS) for AKI among patients undergoing emergency coronary angiography.ConclusionLac12h is more effective for AKI prediction among patients with STEMI than Lacadm and LC. Furthermore, Lac12h and LC enhance the prediction capacity of MRS for AKI among patients after emergency coronary angiography.

Objectives: To evaluate the effects of occurrence and timing of sudden cardiac arrest (SCA) on survival in patients with acute myocardial infarction (AMI) who underwent emergency percutaneous coronary intervention (PCI).Methods: We analyzed 1,956 consecutive patients with AMI with emergency PCI from 2014 to 2018. Patients with cardiac arrest events were identified, and their medical records were reviewed.Results: Patients were divided into non-cardiac arrest group (NCA group, n = 1,724), pre-revascularization cardiac arrest (PRCA group, n = 175), and post-revascularization SCA (POCA group, n = 57) according to SCA timing. Compared to NCA group, PRCA group and POCA group presented with higher brain natriuretic polypeptide (BNP), more often Killip class 3/4, atrial fibrillation, and less often completed recovery of coronary artery perfusion (all p < 0.05). Both patients with PRCA and POCA showed increased 30-day all-cause mortality when compared to patients with NCA (8.0 and 70.2% vs. 2.9%, both p < 0.001). However, when compared to patients with NCA, patients with PRCA did not lead to higher mortality during long-term follow-up (median time 917 days) (16.3 vs. 18.6%, p = 0.441), whereas patients with POCA were associated with increased all-cause mortality (36.3 vs. 18.6%, p < 0.001). Multivariate analysis identified Killip class 3/4, atrial fibrillation, high maximum MB isoenzyme of creatine kianse, and high creatinine as predictive factors for POCA. In Cox regression analysis, POCA was found as a strong mortality-increase predictor (HR, 8.87; 95% CI, 2.26–34.72; p = 0.002) for long-term all-cause death.Conclusions: POCA appeared to be a strong life-threatening factor for 30-day and long-term all-cause mortality among patients with AMI who admitted alive and underwent emergency PCI. However, PRCA experience did not lead to a poorer long-term survival in patients with AMI surviving the first 30 days.

Studies have shown that illuminance and correlated colour temperature (CCT) are strongly correlated with body responses such as circadian rhythm, alertness, and mood. It is worth noting that these responses show a complex and variable coupling, which needs to be solved using accurate mathematical models for the regulation of indoor light parameters. Therefore, in this study, by weighing the evaluations of visual comfort, alertness, valence, and arousal of mood, a multi-objective optimisation mathematical model was developed with constraints conducive to the healthy rhythm. The problem was solved with the multi-objective evolutionary algorithm based on the decomposition differential evolution (MOEA/D-DE) algorithm. Taking educational space as the analysis goal, a dual-parameter setting strategy for illuminance and CCT covering four modes was proposed: focused learning, comfortable learning, soothing learning, and resting state, which could provide a scientific basis for the regulation of the lighting control system. The alertness during class time reached 3.01 compared to 2.34 during break time, showing a good light facilitation effect. The proposed mathematical model and analysis method also have the potential for application in the lighting design and control in other spaces to meet the era of intelligent, highly flexible, and sustainable buildings.

Energy consumption predictions for residential buildings play an important role in the energy management and control system, as the supply and demand of energy experience dynamic and seasonal changes. In this paper, monthly electricity consumption ratings are precisely classified based on open data in an entire region, which includes over 16 000 residential buildings. First, data mining techniques are used to discover and summarize the electricity usage patterns hidden in the data. Second, the particle swarm optimization-K-means algorithm is applied to the clustering analysis, and the level of electricity usage is divided by the cluster centers. Finally, an efficient classification model using a support vector machine as the basic optimization framework is proposed, and its feasibility is verified. The results illustrate that the accuracy and F-measure of the new model reach 96.8% and 97.4%, respectively, which vastly exceed those of conventional methods. To the best of our knowledge, the research on predicting the electricity consumption ratings of residential buildings in an entire region has not been publicly released. The method proposed in this paper would assist the power sector in grasping the dynamic behavior of residential electricity for supply and demand management strategies and provide a decision-making reference for the rational allocation of the power supply, which will be valuable in improving the overall power grid quality.

Prediction of building energy consumption using mathematical modeling is crucial for improving the efficiency of building energy utilization, assisting in building energy consumption planning and scheduling, and further achieving the goal of energy conservation and emission reduction. In consideration of the non-linear and non-smooth characteristics of building energy consumption time series data, a short-term, hybrid building energy consumption prediction model combining variational mode decomposition (VMD), a simulated annealing (SA) algorithm, and a deep belief network (DBN) is proposed in this study. In the proposed VMD-SA-DBN model, the VMD algorithm decomposes the time series into different modes to reduce the fluctuation of the data. The SA-DBN prediction model is built for each mode separately, and the DBN network structure parameters are optimized by the SA algorithm. The prediction results of each model are aggregated and reconstructed to obtain the final prediction output. The validity and prediction performance of the proposed model is evaluated on a publicly available dataset, and the results show that the proposed new model significantly improves the accuracy and stability of building energy consumption prediction compared with several typical machine learning methods. The mean absolute percent error (MAPE) of the VMD-SA-DBN model is 63.7%, 65.5%, 46.83%, 64.82%, 44.1%, 36.3%, and 28.3% lower than that of the long short-term memory (LSTM), gated recurrent unit (GRU), VMD-LSTM, VMD-GRU, DBN, SA-DBN, and VMD-DBN models, respectively. The results will help managers formulate more-favorable low-energy emission reduction plans and improve building energy efficiency.

In order to promote the development and construction of smart cities, the massive equipment requirements of sensing terminals increased the pressure on urban site resource allocation. The light pole is suitable for carrying various urban functional equipment to form a smart street lighting system, which can provide rich site resources for the large-scale construction of urban functional facilities such as 5G micro base stations. However, the selection and combination of equipment mounted in the smart street lighting system only focus on the functional superposition at the physical level, without considering the relevance of each subsystem in practical application scenarios. Therefore, this study proposed a 5G micro base station location model based on a smart street lighting system. The correlation and cooperativity between 5G micro base stations and mounted devices were fully considered, and a universal system-level location selection index was developed to realize rational utilization of urban space site resources and intelligent linkage between subsystems. The results showed that the model is significantly effective for functional areas with different road network characteristics and provides practical, robust, effective, and accurate help for similar location selection problems.