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Plasma arc additive manufacturing (PAAM) stands out for its remarkable efficient utilization and good performance reliability, making it a highly favored additive manufacturing technique. This study delves into the tensile deformation below δ-solvus temperature of PAAM IN718 alloy with complete heat treatment. The results show that the peak stresses are decreased from 1069 to 1189 MPa at 550 °C to 620–1059 MPa at 750 °C. Through the orientation distribution function, the domain textures for hot deformation were identified as cube texture and Rt-Goss texture {110}. The results of the observations on the substructure and grain boundary components of the deformed alloy confirmed that the onset temperature of recrystallization in PAAM alloy is approximately 750 °C. The recrystallization nucleation mechanism of PAAM IN718 alloys was identified as discontinuous dynamic recrystallization. A deformation constitutive equation for hot deformation was established using the Zener-Hollomon parameter, with an average activation energy of 508.93 kJ/mol. A processing map for the hot working of PAAM alloy was developed, and the reasonable processing domains are located at a temperature 700–750 °C/strain rate 10−2–10−1 s−1 with a power dissipation efficiency range of 0.41–0.47. The high-temperature fracture mechanism of PAAM alloy was discussed, identifying the important inducements for high-temperature fracture failure including carbides, residual Laves phase, and δ phase in the PAAM alloy.

Abstract Background This study was to explore the relationship between cardiovascular health (CVH) and the risk of all-cause mortality in patients with osteoarthritis (OA). Methods This cohort study retrieved the data of 3642 patients with OA aged ≥ 20 years from the 2007—2018 National Health and Nutrition Examination Survey (NHANES). CVH was evaluated based on Life’s Essential 8 (LE8) includes diet, physical activity, nicotine exposure, sleep health, body mass index, blood lipids, blood glucose, and blood pressure. The outcome of all-cause mortality was assessed using the death certificate records of participants from the National Death Index. Variables that might affect all-cause mortality were used as covariates. The weighted univariate COX proportional hazards model was used to explore the association between each covariate and all-cause mortality. The weighted univariate and multivariate COX proportional hazards models were used to explore the association between different CVH levels and all-cause mortality. A restricted cubic spline (RCS) curve was plotted to show the association between different CVH levels and all-cause mortality in OA patients. Hazard ratio (HR) and 95% confidence interval (CI) were calculated. Results Findings show that people with moderate CVH (HR = 0.67, 95% CI = 0.45—0.98) and high CVH (HR = 0.47, 95% CI = 0.26—0.87) were associated with reduced risk of all-cause mortality in patients with OA. The HR of all-cause mortality in patients with OA decreased by 0.12 as per 10 points increase of LE8 score (HR = 0.81, 95% CI = 0.73—0.90). The RCS curve revealed that the HR of all-cause mortality decreased with the increase in LE8 score. The survival probability of patients in the high CVH group was higher than the moderate CVH group and low CVH group (p = 0.002). Conclusion Moderate-to-high CVH is associated with a decreased risk of all-cause mortality in patients with OA. These findings might provide a reference for the formulation of prognosis improvement strategies for the management of patients with OA.

Abstract In the geothermal development of hot dry rock (HDR), both the drilling of the wellbore and the heat exchange of the heat reservoir involve the effects of different cold and hot conditions on the high-temperature rock mass. The testing machine for rock mechanics was used to conduct a uniaxial compression test and carry out micro testing on the treated samples; furthermore, with the help of scanning electron microscopy the fracture mechanism of granite subjected to different temperatures and cooling methods was studied. The results show: (1) With the gradual increase in temperature, the compressive strength of granite under the two cooling methods gradually decreases. (2) The failure modes of the samples under the two cooling methods are mainly shear failure of the "Y" type. The degree of damage of the sample under water cooling is significantly greater than that under natural cooling. Electron micrographs could confirm these results. (3) It can be obtained by testing the mineral composition and element changes of granite at different temperatures. When the temperature reaches 600℃, its change is more pronounced. The results of this study can provide a theoretical reference for the failure of the wellbore and the degree of fracture of the thermal reservoir rock mass during geothermal development.

Abstract Background Computed tomography (CT)-guided biopsy (CTB) procedures are commonly used to aid in the diagnosis of pulmonary nodules (PNs). When CTB findings indicate a non-malignant lesion, it is critical to correctly determine false-negative results. Therefore, the current study was designed to construct a predictive model for predicting false-negative cases among patients receiving CTB for PNs who receive non-malignant results. Materials and methods From January 2016 to December 2020, consecutive patients from two centers who received CTB-based non-malignant pathology results while undergoing evaluation for PNs were examined retrospectively. A training cohort was used to discover characteristics that predicted false negative results, allowing the development of a predictive model. The remaining patients were used to establish a testing cohort that served to validate predictive model accuracy. Results The training cohort included 102 patients with PNs who showed non-malignant pathology results based on CTB. Each patient underwent CTB for a single nodule. Among these patients, 85 and 17 patients, respectively, showed true negative and false negative PNs. Through univariate and multivariate analyses, higher standardized maximum uptake values (SUVmax, P = 0.001) and CTB-based findings of suspected malignant cells (P = 0.043) were identified as being predictive of false negative results. Following that, these two predictors were combined to produce a predictive model. The model achieved an area under the receiver operating characteristic curve (AUC) of 0.945. Furthermore, it demonstrated sensitivity and specificity values of 88.2% and 87.1% respectively. The testing cohort included 62 patients, each of whom had a single PN. When the developed model was used to evaluate this testing cohort, this yielded an AUC value of 0.851. Conclusions In patients with PNs, the predictive model developed herein demonstrated good diagnostic effectiveness for identifying false-negative CTB-based non-malignant pathology data.

Abstract Objective Based on resting-state electroencephalography (EEG) evidence, this study aimed to explore the relationship and pathways between EEG-mediated physical function and cognitive function in older adults with cognitive impairment. Methods A total of 140 older adults with cognitive impairment were recruited, and data on their physical function, cognitive function, and EEG were collected. Pearson correlation analysis, one-way analysis of variance, linear regression analysis, and structural equation modeling analysis were conducted to explore the relationships and pathways among variables. Results FP1 theta (effect size = 0.136, 95% CI: 0.025–0.251) and T4 alpha2 (effect size = 0.140, 95% CI: 0.057–0.249) were found to significantly mediate the relationship. The direct effect (effect size = 0.866, 95% CI: 0.574–1.158) and total effect (effect size = 1.142, 95% CI: 0.848–1.435) of SPPB on MoCA were both significant. Conclusion Higher physical function scores in older adults with cognitive impairment were associated with higher cognitive function scores. Left frontal theta and right temporal alpha2, as key observed indicators, may mediate the relationship between physical function and cognitive function. It is suggested to implement personalized exercise interventions based on the specific physical function of older adults, which may delay the occurrence and progression of cognitive impairment in older adults with cognitive impairment.

Summary: Background: Masked hypertension is associated with target organ damage (TOD) and adverse health outcomes, but whether antihypertensive treatment improves TOD in patients with masked hypertension is unproven. Methods: In this multicentre, randomised, double-blind, placebo-controlled trial at 15 Chinese hospitals, untreated outpatients aged 30–70 years with an office blood pressure (BP) of

The concrete that absorbs carbon dioxide during the mixing process, termed as carbon-mixed concrete, has become a hot research topic under the background of dual carbon goals. However, the workability of concrete significantly decreases even with the absorption of a minimal amount of carbon dioxide, indicating a potential challenge in its practical application. Therefore, this study investigated the effects of incorporating 0.5%, 1.0%, and 2.0% carbon dioxide (relative to the mass of cementitious materials) and retarder on the mechanical properties and microstructure of concrete through mercury intrusion, X-ray diffraction, and scanning electron microscopy, revealing the mechanism of retarder improving carbon-mixed concrete. The results indicated that the flowability and 28-day compressive strength of concrete mixed with 0.5% carbon dioxide decreased by 68.75% and 10.77%, respectively. However, after adding 0.25% retarder, these values for the carbon-mixed concrete only decreased by 18.75% and 3.52%. Meanwhile, the introduction of carbon dioxide can form carbonates and carboaluminates and refine the internal pores of the concrete matrix. This study proposes an effective method to improve the performance of carbon-mixed concrete, which can promote the efficient absorption of carbon dioxide in the concrete industry.

Abstract In recent years, corneal refractive surgery has been widely used in clinics as an effective means to restore vision and improve the quality of life. When choosing myopia-refractive surgery, it is necessary to comprehensively consider the differences in equipment and technology as well as the specificity of individual patients, which heavily depend on the experience of ophthalmologists. In our study, we took advantage of machine learning to learn about the experience of ophthalmologists in decision-making and assist them in the choice of corneal refractive surgery in a new case. Our study was based on the clinical data of 7,081 patients who underwent corneal refractive surgery between 2000 and 2017 at the Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences. Due to the long data period, there were data losses and errors in this dataset. First, we cleaned the data and deleted the samples of key data loss. Then, patients were divided into three groups according to the type of surgery, after which we used SMOTE technology to eliminate imbalance between groups. Six statistical machine learning models, including NBM, RF, AdaBoost, XGBoost, BP neural network, and DBN were selected, and a ten-fold cross-validation and grid search were used to determine the optimal hyperparameters for better performance. When tested on the dataset, the multi-class RF model showed the best performance, with agreement with ophthalmologist decisions as high as 0.8775 and Macro F1 as high as 0.8019. Furthermore, the results of the feature importance analysis based on the SHAP technique were consistent with an ophthalmologist’s practical experience. Our research will assist ophthalmologists in choosing appropriate types of refractive surgery and will have beneficial clinical effects.

Purposes At present, the data driven method represented by deep learning has been widely used in power transient stability analysis. However, the existing transient stability models for researching data driving have some problems, such as limited generalization ability and insufficient model accuracy, when facing small samples, weak samples, and other actual scenarios. In order to improve the expression ability of the model, a refined transient stability assessment method is proposed in this paper according to operation data and fault data. Methods First, four fault information characteristics, namely fault time, fault location, disturbed line, and load level, are constructed according to the transient stability mechanism model of power system. Then, two feature fusion methods, parallel fusion and serial fusion, are proposed to realize the unified expression of operation features and fault features. The influence of multi-source feature fusion on transient stability analysis model is analyzed in depth. Findings The experimental results of the New England system example show that the transient stability analysis method based on multi-source data hybrid drive is conducive to improving the accuracy of the transient stability assessment model, and still has a high accuracy in practical scenarios such as small samples and weak samples.

Ionic liquid electrolytes (ILEs), distinguished by their nonvolatility and high thermal stability, demonstrate considerable compatibility with silicon anodes and thus are an ideal alternative electrolyte for lithium-ion batteries. However, substantial challenges are to be addressed, notably the poor rate capability predominantly due to excessively tight Li+-coordination in the solvation sheath. Here, we introduce an innovative ILE-based electrolyte tailored for micro-sized silicon (μSi) anodes to address the issue of rate capability. By incorporating nonflammable sulfolane molecules into the primary Li+-solvation sheath, we concurrently enhance de-solvation kinetics and ion-conducting capability of solid electrolyte interphase (SEI). The primary sulfolane-mediated Li+-solvation configuration weakens de-solvation barrier and strengthens inorganic-rich SEI, while impeding the precipitation of organic cations in the SEI formation. The μSi electrode with the sulfolane-mediated ILE delivers an impressive rate capability of 782.5 mA h g–1 at a high current density of 12 A g–1 over 500 cycles Accelerating rate calorimetry further verifies remarkable thermal stability of the derived SEI and superior thermal safety of the sulfolane-mediated ILE compared to the commonly used electrolyte. This work provides an effective way to boost the rate capability of μSi anodes based on ILEs without compromising the thermal safety for developing high-performance lithium-ion batteries.

With the advancements of IoT and blockchain, a novel era has emerged in the domain of smart building systems. At the same time, it also brings some problems and challenges. Most traditional solutions typically utilize the fully-replicated storage strategy that results in high storage costs, while recent solutions like coded blockchain may compromise query efficiency. Moreover, traditional reputation-based consensus schemes do not consider dynamic situations, limiting scalability. To handle these problems, we propose a novel hierarchical message aggregation scheme and a classified storage method under Reed–Solomon (RS) coding to minimize storage overhead while ensuring data recoverability and query performance. Additionally, we introduce a dynamic incentive reputation consensus mechanism to tackle scalability challenges such as preventing node monopolies, promoting new node integration, and enhancing fault tolerance. Through theoretical analysis and experimental simulation, the proposed scheme demonstrates a high degree of decentralization and scalability. Our scheme achieves a 20% reduction in the Gini coefficient compared to other approaches. Furthermore, our scheme can save 19 of storage overhead compared to traditional solutions while maintaining high query performance.

New grid equipment startup programs are widely used in various countries to regulate the commissioning of new equipment; these programs have unique differences in terms of strictness, information asymmetry, and complexity relative to other types of startup programs. With respect to rule-based generation methods, because the method of revising the rules weakens their migration ability, it is difficult to adapt these methods to the status quo of high-speed power grid construction; moreover, most of the current generation methods based on deep learning improve upon the rule-based methods but do not eliminate the rules of the constraints. Therefore, this paper presents a fusion topology for generating a new grid equipment startup scheme, which generates the scheme from end to end. The method utilizes the powerful processing capabilities of the GATv2 model and the ERNIE-GEN model for topology and text, respectively. The device type-based coding strategy and the scheme complexity-based self-attention layer selection strategy are used in the GATv2-based device identification model to address information asymmetry and complexity variability, and the device information modification strategy is applied to solve the strictness variability problem in the ERNIE-GEN-based scheme generation model. Finally, through the testing and verification of field data from four types of new equipment startup schemes in real power grids, it is verified that the method can effectively generate new equipment startup schemes for power grids, and the reasonableness and efficiency of the three strategies are verified through ablation experiments, which verify that the method can effectively generate new equipment startup schemes for power grids that meet the requirements of real power grids.

In the field of wireless communication, transmitter localization technology is crucial for achieving accurate source tracking. However, the extant methodologies for localization face numerous challenges in wireless sensor networks (WSNs), particularly due to the constraints posed by the sparse distribution of sensors across large areas. We present DSLoc, a deep learning-based approach for transmitter localization in sparse WSNs. Our method is based on an improved high-resolution network model in neural networks. To address localization in sparse wireless sensor networks, we design efficient feature enhancement modules, and propose to locate transmitter locations in the heatmap using an image centroid-based method. Experiments conducted on WSNs with a 0.01% deployment density demonstrate that, compared to existing deep learning models, our method significantly reduces the transmitter miss rate and improves the localization accuracy by more than double. The results indicate that the proposed method offers more accurate and robust performance in sparse WSN environments.

Energy is the fundamental prerequisite for human survival and development, as well as the driving force behind the progress of human civilization [...]