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Background and aimsCoffee contains many bioactive compounds, and its inconsistent association with subclinical atherosclerosis has been reported in observational studies. In this Mendelian randomization study, we investigated whether genetically predicted coffee consumption is associated with subclinical atherosclerosis, as well as the role of potential mediators.MethodsWe first conducted a two-sample Mendelian randomization analysis to examine the causal effect of coffee and its subtypes on subclinical atherosclerosis inferred from coronary artery calcification (CAC). Next, the significant results were validated using another independent dataset. Two-step Mendelian randomization analyses were utilized to evaluate the causal pathway from coffee to subclinical atherosclerosis through potential mediators, including blood pressure, blood lipids, body mass index, and glycated hemoglobin. Mendelian randomization analyses were performed using the multiplicative random effects inverse-variance weighted method as the main approach, followed by a series of complementary methods and sensitivity analyses.ResultsCoffee, filtered coffee, and instant coffee were associated with the risk of CAC (β = 0.79, 95% CI: 0.12 to 1.47, p = 0.022; β = 0.66, 95% CI: 0.17 to 1.15, p = 0.008; β = 0.66, 95% CI: 0.20 to 1.13, p = 0.005; respectively). While no significant causal relationship was found between decaffeinated coffee and CAC (β = −1.32, 95% CI: −2.67 to 0.04, p = 0.056). The association between coffee and CAC was validated in the replication analysis (β = 0.27, 95% CI: 0.07 to 0.48, p = 0.009). Body mass index mediated 39.98% of the effect of coffee on CAC (95% CI: 9.78 to 70.19%, p = 0.009), and 5.79% of the effect of instant coffee on CAC (95% CI: 0.54 to 11.04%, p = 0.030).ConclusionOur study suggests that coffee other than decaffeinated coffee increases the risk of subclinical atherosclerosis inferred from CAC. Body mass index mediated 39.98 and 5.79% of the causal effects of coffee and instant coffee on CAC, respectively. Coffee should be consumed with caution, especially in individuals with established cardiovascular risk factors, and decaffeinated coffee appears to be a safer choice.

Abstract Background and objective Cardiac rehabilitation (CR) has been demonstrated to improve outcomes in patients with acute myocardial infarction (AMI) after percutaneous coronary intervention (PCI). However, the optimal CR initiation time and duration remain to be determined. This study aimed to explore the impact of the time factors on the CR outcomes in AMI patients who received PCI by the method of meta-regression analysis. Methods We searched five databases (PubMed, Embase, Cochrane Library, Web of Science and Google scholar) up to October 31, 2023. Meta-regression analysis was utilized to explore the impact of the time factors on the effect sizes. Subgroups with more than 3 studies were used for meta-regression analysis. Results Our analysis included 16 studies and a total of 1810 patients. The meta-regression analysis revealed that the initiation time and duration of CR had no significant impact on the occurrence of arrhythmia, coronary artery restenosis and angina pectoris. The initiation time and duration of CR also had no significant impact on the changes in left ventricular ejection fraction (LVEF, starting time: estimate = 0.160, p = 0.130; intervention time: estimate = 0.017, p = 0.149), left ventricular end-diastolic volume (LVEDV, starting time: estimate = − 0.191, p = 0.732; intervention time: estimate = − 0.033, p = 0.160), left ventricular end-systolic volume (LVESV, starting time: estimate = − 0.301, p = 0.464; intervention time: estimate = 0.015, p = 0.368) and 6-minute walk test (6MWT, starting time: estimate = − 0.108, p = 0.467; intervention time: estimate = 0.019, p = 0.116). Conclusion Implementation of CR following PCI in patients with AMI is beneficial. However, in AMI patients, there is no significant difference in the improvement of CR outcomes based on different CR starting times within 1 month after PCI or different durations of the CR programs. It indicates that it is feasible for patients with AMI to commence CR within 1 month after PCI and continue long-term CR, but the time factors which impact CR are intricate and further clinical research is still needed to determine the optimal initiation time and duration of CR.

Hydropower units are integral to sustainable energy production, and their operational reliability hinges on accurate status prediction. This paper introduces an innovative hybrid deep learning model that synergistically integrates a Temporal Convolutional Network (TCN), a Residual Short-Term LSTM (REST-LSTM) network, a Gated Recurrent Unit (GRU) network, and the tuna swarm optimization (TSO) algorithm. The model was meticulously designed to capture and utilize temporal features inherent in time series data, thereby enhancing predictive performance. Specifically, the TCN effectively extracts critical temporal features, while the REST-LSTM, with its residual connections, improves the retention of short-term memory in sequence data. The parallel incorporation of GRU further refines temporal dynamics, ensuring comprehensive feature capture. The TSO algorithm was employed to optimize the model’s parameters, leading to superior performance. The model’s efficacy was empirically validated using three datasets—unit flow rate, guide vane opening, and maximum guide vane water temperature—sourced from the Huadian Electric Power Research Institute. The experimental results demonstrate that the proposed model significantly reduces both the maximum and average prediction errors, while also offering substantial improvements in forecasting accuracy compared with the existing methodologies. This research presents a robust framework for hydropower unit operation prediction, advancing the application of deep learning in the hydropower sector.

The reliability and stability of marine diesel engines are pivotal to the safety and economy of maritime operations. Accurate and efficient prediction of the states of these engines is essential for performance evaluation and operational continuity. This paper introduces a novel hybrid deep learning model, the multi-dimensional global temporal predictive (MDGTP) model, designed for synchronous multi-state prediction of marine diesel engines. The model incorporates parallel multi-head attention mechanisms, an enhanced long short-term memory (LSTM) with interleaved residual connections, and gated recurrent units (GRUs). Additionally, we propose a dynamic arithmetic tuna optimization algorithm, which synergizes tuna swarm optimization (TSO), and the arithmetic optimization algorithm (AOA) for hyperparameter optimization, thereby enhancing prediction accuracy. Comparative experiments using actual marine diesel engine data demonstrate that our model outperforms the LSTM, GRU, LSTM–GRU, support vector regression (SVR), random forest (RF), Gaussian process regression (GPR), and back propagation (BP) models, achieving the lowest root mean squared error (RMSE) and mean absolute error (MAE), as well as the highest Pearson correlation coefficient across three sampling periods. Ablation studies confirm the significance of each component in improving prediction accuracy. Our findings validate the efficacy of the proposed MDGTP model for predicting the multi-dimensional operating states of marine diesel engines.

In recent years, the pipe shed advanced support method has emerged as a new technique for excavating tunnels in weak surrounding rock. However, the necessity to maintain a certain inclination angle when constructing large pipe sheds unavoidably increases the excavation and lining quantities. Consequently, as the length of the pipe shed increases, construction errors also grow, resulting in larger excavations and backfilling works, thereby making it difficult to control the quality of pipe shed installations and limiting the development of the pipe shed method. Faced with the challenges presented by tunnel support construction as part of subway tunnel construction, this paper is based on the Songhuai Youyuan Station tunnel project involving Zhengzhou Metro Line 9. Field experiments were conducted, using high-torque horizontal drilling machines and pipe shed guiding technology to successfully complete the installation of a 208 m long pipe shed in a single operation (the longest in the world). Through case analysis and technological innovation, a feasible and effective drilling technology scheme was proposed. Compared with traditional methods, the key technology for installing super-long pipe sheds in a single operation reduced the construction time by 35% and construction costs by 25%, providing valuable insights for similar projects.

IntroductionHyperuricemia, characterized by elevated serum uric acid levels, has garnered significant attention in cardiovascular research due to its potential association with coronary heart disease (CHD). While some studies suggest hyperuricemia as a risk factor of CHD, others present conflicting findings. A systematic review and dose-response meta-analysis is warranted to comprehensively summarize the previous studies and determine the association between hyperuricemia and CHD, thereby supporting clinical practice and future studies in this field.MethodsIn this study, we will comprehensively search Medline, EMBase, Cochrane Central, ICTRP, and ClinicalTrials.gov, from inception to December 31, 2024. Prospective or retrospective cohort studies and case-control studies investigating the association between hyperuricemia and CHD will be included. Two independent reviewers will conduct study selection, data extraction, and risk of bias assessment. The primary outcome will be the pooled relative risk of CHD associated with hyperuricemia by using random-effect model. Dose-response meta-analysis will be performed with linear and non-linear model to explore the the magnitude and direction of the association between serum uric acid levels and CHD risk. Subgroup analyses will be conducted based on uric acid test approaches and corresponding cut-off values and human races. Sensitivity analyses will assess the robustness of the results with leave-one-out method, while publication bias will be evaluated using funnel plots, Egger's test, and Begg's test. We will further use GRADE to evaluate the quality of the evidences provided by our systematic review.Expected resultsFrom this systematic review and dose-response meta-analysis, we hope out findings will provide reliable conclusion and data support on the association between hyperuricemia and CHD. The transparent and replicable methodologies outlined in this protocol contribute to advancing understanding of hyperuricemia as a potentially modifiable risk factor for CHD, thus supporting evidence-based strategies for cardiovascular disease management.ConclusionsThis protocol describes a rigorous plan to systematically review and analyze the quantitative association between hyperuricemia and CHD risk. In a word, we will help further clinical practice and scientific studies in this field.Trial registrationThis protocol was registered in PROSPERO CRD42024538553.

High-performance vector hydrophones have been gaining attention for underwater target-monitoring applications. Nevertheless, there exists the mutual constraint between sensitivity and bandwidth of a single hydrophone. To solve this problem, a four-unit array piezoelectric bionic MEMS vector hydrophone (FPVH) was developed in this paper, which has a cross-beam and a bionic fish-lateral-line-nerve-cell-cilia unit array structure. Simulation analysis and optimization in the design of the bionic microstructure have been performed by COMSOL 6.1 software to determine the structure dimensions and the lead zirconate titanate (PZT) thin film distribution. The FPVH was manufactured using MEMS technology and tested in a standing wave bucket. The results indicate that the FPVH has a sensitivity of up to −167.93 dB@1000 Hz (0 dB = 1 V/μPa), which is 12 dB higher than that of the one-unit piezoelectric MEMS vector hydrophone (OPVH). Additionally, the working bandwidth of the FPVH reaches 20 Hz~1200 Hz, exhibiting a good cosine curve with an 8-shape. This work paves a new way for the development of multi-unit piezoelectric vector hydrophones for underwater acoustic detectors.

Metal oxide semiconductors (MOSs) hold great promise for electronic devices such as gas sensors. The utilization of ZnO as a conductometric gas sensor material can be traced back to its early stages; however, its application has primarily been limited to high-temperature environments. A gas sensor based on highly porous and interconnected 3D networks of ZnO tetrapod (ZnO-T) micro-nano structures was fabricated via an easy chemical vapor deposition (CVD) method. Homemade instruments were utilized to evaluate the gas-sensing of the sample at room temperature. It exhibited good gas-sensing at room temperature, particularly with a response of up to 338.80% toward 1600 ppm ethanol, while also demonstrating remarkable repeatability, stability, and selectivity. Moreover, the unique gas-sensing properties of ZnO-T at room temperature can be reasonably explained by considering the effect of van der Waals forces in physical adsorption and the synergistic effect of carrier concentration and mobility. The aforementioned statement presents an opportunity for the advancement of gas sensors utilizing ZnO at room temperature.

Unmanned surface vehicle (USV) is the future development direction of ships, but few studies have focused on USV’s energy optimization based on visual perception. An energy optimization strategy based on visual object detection is developed for USV. A visual target recognition method is proposed by combining YOLOv5 and DeepSORT. Visual recognition results are fused with radar targets to support route plan for energy optimization of USV. By dynamically adjusting the threshold of visual target recognition with the target number provided by radar, the target detection result is more accurate. Experimental results show that the proposed target detection method has the best performance than other commonly used methods, MOTA of the proposed method reaches 87.40%, and the YOLOv4 method, CenterTrack and FairMOT are 85.18%, 64.97% and 46.39% respectively. And the energy consumption optimization can be dynamically achieved by continuously analyzing the speed and path of the USV and predicting fuel consumption.

Hydropower generation has become an important guarantee for social and economic evolution. The hydropower turbine is the main power device of hydropower generation. And the anomaly state of the turbine is important to the operation and maintenance of the turbine. The complex signals measured by the hydropower turbine sensor are decomposed into the sub signals employing variational modal decomposition (VMD). The accuracy of anomaly detection is improved by applying VMD. A hydropower turbine anomaly detection application combining VMD and hierarchical temporal memory (HTM) is developed. Specificities of spinning-tree, cluster, HTM and the proposed method are 0.730, 0.690, 0.820, 0.945, respectively. Accuracy are 0.872, 0.873, 0.931, 0.959, respectively, and F1 scores are 0.923, 0.924, 0.959, 0.976, respectively. The proposed method can be applied to other anomaly detection applications.

Background: Noninvasively predicting kidney tubulointerstitial fibrosis is important because it's closely correlated with the development and prognosis of chronic kidney disease (CKD). Most studies of shear wave elastography (SWE) in CKD were limited to non-linear statistical dependencies and didn't fully consider variables' interactions. Therefore, support vector machine (SVM) of machine learning was used to assess the prediction value of SWE and traditional ultrasound techniques in kidney fibrosis. Methods: We consecutively recruited 117 CKD patients with kidney biopsy. SWE, B-mode, color Doppler flow imaging ultrasound and hematological exams were performed on the day of kidney biopsy. Kidney tubulointerstitial fibrosis was graded by semi-quantification of Masson staining. The diagnostic performances were accessed by ROC analysis. Results: Tubulointerstitial fibrosis area was significantly correlated with eGFR among CKD patients (R = 0.450, P 10%), higher than either traditional ultrasound, or SWE (AUC, 0.6735 [sensitivity, 67.74%; specificity, 65.45%]; 0.5391 [sensitivity, 55.56%; specificity, 53.33%] respectively. Delong test, p 50%), higher than other methods (Delong test, p

BackgroundThere is growing emphasis on the cardiotoxicity research over the past 12 years. To look for the hotspots evolution and to explore the emerging trends in the field of cardiotoxicity, publications related to cardiotoxicity were acquired from the Web of Science Core Collection on August 2, 2022.MethodsWe used the CiteSpace 5.8 R3 and VOSviewer 1.6.18 to perform bibliometric and knowledge-map analysis.ResultsA total of 8,074 studies by 39,071 authors from 6,530 institutions in 124 countries or regions were published in different academic journals. The most productive country was absolutely the United States, and the University of Texas MD Anderson Cancer Center was the institution with the largest output. Zhang, Yun published the most articles, and the author who had the most frequent co-citations was Moslehi, Javid. New England Journal of Medicine was the most frequently cited journals in this field. Mechanisms of cardiotoxicity have received the most attention and was the main research directions in the field. The disease of cardiotoxicity together with the related risk factors are potential research hotspots. Immune checkpoint inhibitor and myocarditis are two recently discussed and rapidly expanding research topic in the areas of cardiotoxicity.ConclusionsThis bibliometric analysis provided a thorough analysis of the cardiotoxicity, which would provide crucial sources of information and concepts for academics studying this area. As a rapidly expanding field in cardiology, the related field of cardiotoxicity will continue to be a focus of research.

Anomaly detection for hydropower turbine unit is a requirement for the safety of hydropower system. An unsupervised anomaly detection method employing variational modal decomposition (VMD) and deep autoencoder is proposed. VMD is employed to the data collected by multiple sensors to obtain the sub signal of each data. These sub signals in each time-period constitute two-dimensional data. The autoencoder based on convolutional neural network is used to complete unsupervised learning, and the reconstruction residual of autoencoder is used for anomaly detection. The experimental results show that the deep autoencoder can increase the interval between abnormal and normal data distribution, and VMD can effectively reduce the number of samples in the overlapping area. Compared with traditional autoencoder method, the proposed method improves the recall, precision and F1 scores by 0.140, 0.205 and 0.175, respectively. The proposed method achieves better anomaly detection performance than other methods.

With the development of unmanned systems, more and more attentions are paid to the energy and power systems of data-driven ships. The autonomy of unmanned ships puts forward urgent requirements for the monitoring and prediction of the energy and power system of ships. Aiming at the state prediction for marine diesel engine, an improvement method based on variational modal decomposition (VMD) and long short-term memory (LSTM) is proposed in this paper. The sub signals are obtained by decomposing the signal to be predicted through VMD, the sub signals and resident signal are all predicted with LSTM, and the reconstruction prediction signal is obtained by sum all the predicted sub signals and resident signal. Compared with LSTM, ESN, and SVR methods, the proposed method reduces the prediction errors significantly. Compared with LSTM, the RE errors of the two sensors are reduced by 49.79% and 56.32% respectively, and the RMSE errors are reduced by 34.65% and 27.71% respectively. The performance of this method is better than other methods, and it has sufficient accuracy performance for state prediction of marine diesel engine.

The internal flow of hydrodynamic retarder is a three-dimensional, complex, viscous, and unstable gas–liquid two-phase flow. The importance of numerical simulation of a hydrodynamic retarder has been paid attention to, but the research on the two-phase flow pattern of the retarder is rarely. Flow pattern diagnosis plays an important role in the study of two-phase flow because the premise of establishing the mathematical model of the two-phase flow is to accurately determine the flow structure of the flow system. Based on the numerical simulation and experimental research, this study found the corresponding relationship between the two-phase flow distribution inside the hydrodynamic retarder and the filling rate (expressed as “q”). The result shows that, when q = 0.1–0.2, the liquid in the working chamber is less. Under the action of centrifugal force, the liquid mainly gets distributed in the outer ring of the circulating circle, forming a laminar flow. When q = 0.3–0.4, the flow pattern of the gas–liquid two-phase flow changes from the laminar flow to slug flow. When q = 0.5–0.9, with a further increase in the filling ratio, the flow becomes a dispersed bubble flow. This paper provides an effective method for the analysis and identification of the two-phase flow pattern of a hydrodynamic retarder.

ObjectiveTo compare the efficacy and safety of bioresorbable scaffolds (BRS) with drug-eluting stents (DES) in patients with myocardial infarction undergoing percutaneous coronary interventions (PCI).MethodsWe performed a systematic review and meta-analysis of randomized controlled trials (RCTs) comparing BRS with DES on clinical outcomes with at least 12 months follow-up. Electronic databases of PubMed, CENTRAL, EMBASE, and Web of Science from inception to 1 March 2022 were systematically searched to identify relevant studies. The primary outcome of this study was the device-oriented composite endpoint (DOCE) consisting of cardiac death, target-vessel myocardial infarction, and target lesion revascularization. Secondary outcomes were a composite of major adverse cardiac events (MACE, all-cause death, target-vessel myocardial infarction, or target vessel revascularization) and the patient-oriented composite endpoint (POCE, defined as a composite of all-cause death, myocardial infarction, or revascularization). The safety outcomes were definite/probable device thrombosis and adverse events.ResultsFour randomized clinical trials including 803 participants with a mean age of 60.5 ± 10.8 years were included in this analysis. Patients treated with BRS had a higher risk of the DOCE (RR 1.62, 95% CI: 1.02–2.57, P = 0.04) and MACE (RR 1.77, 95% CI: 1.02–3.08, P = 0.04) compared with patients treated with DES. No significant difference on the POCE (RR 1.33, 95% CI: 0.89–1.98, P = 0.16) and the definite/probable device thrombosis (RR 1.31, 95% CI: 0.46–3.77, P = 0.61) were observed between BRS and DES. No treatment-related serious adverse events were reported.ConclusionBRS was associated with a higher risk of DOCE and MACE compared with DES in patients undergoing PCI for myocardial infarction. Although this seems less effective in preventing DOCE, BRS appears as safe as DES.Systematic review registration[https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=321501], identifier [CRD 42022321501].

In response to the growing demand for high-sensitivity accelerometers in vector hydrophones, a piezoelectric MEMS accelerometer (PMA) was proposed, which has a four-cantilever beam integrated inertial mass unit structure, with the advantages of being lightweight and highly sensitive. A theoretical energy harvesting model was established for the piezoelectric cantilever beam, and the geometric dimensions and structure of the microdevice were optimized to meet the vibration pickup conditions. The sol-gel and annealing technology was employed to prepare high-quality PZT thin films on silicon substrate, and accelerometer microdevices were manufactured by using MEMS technology. Furthermore, the MEMS accelerometer was packaged for testing on a vibration measuring platform. Test results show that the PMA has a resonant frequency of 2300 Hz. In addition, there is a good linear relationship between the input acceleration and the output voltage, with V = 8.412a − 0.212. The PMA not only has high sensitivity, but also has outstanding anti-interference ability. The accelerometer structure was integrated into a vector hydrophone for testing in a calibration system. The results show that the piezoelectric vector hydrophone (PVH) has a sensitivity of –178.99 dB@1000 Hz (0 dB = 1 V/μPa) and a bandwidth of 20~1100 Hz. Meanwhile, it exhibits a good “8” shape directivity and consistency of each channel. These results demonstrate that the piezoelectric MEMS accelerometer has excellent capabilities suitable for use in vector hydrophones.

Unmanned surface vehicles (USV) is the development trend of future ships, and it will be widely used in various kinds of marine tasks. Obstacle avoidance is one key technology for autonomous navigation of USV. Convolutional neural network based obstacle classification and detection method is applied to USV visual images in environment sensing task. To solve the problem of low detection and classification accuracy of obstacles in the visual inspection of USV, a bidirectional feature pyramid networks is proposed combining hybrid network architecture of ResNet and improved DenseNet. The proposed method can further enhance the detection and classification some types of obstacles by using the underlying multi-layer detail features and high-level strong semantic features in the network architecture. The detection and classification performance of the proposed method is evaluated on a self built dataset. Ablation experiments and performance tests on open datasets are also employed. The experimental results show that the proposed algorithm has best performance for obstacles detection, and it is more suitable for autonomous navigation of USV.

Great changes have been brought about by the coastal environment when the economy develops rapidly. Coastal environmental monitoring is the basis and technical guarantee for coastal environmental protection supervision and management. It is one of the important tasks to detect and timely discover coastal seawater anomalies. Usually, a single sensor cannot determine whether the coastal environment or ship operation is an anomaly. Recently, an unmanned surface vehicle for coastal environment monitoring was developed, and stacked autoencoders are used for seawater anomaly detection using multisensor data fusion methods. The multisensor data of pH, conductivity, and ammonia nitrogen are employed to judge the anomaly of seawater. The mean, standard deviation, mean square root, and normalized power spectrum features of multisensor data are extracted, and a stacked autoencoder is employed to fuse these features for anomaly detection. The proposed method is feasible and effective for anomaly detection of coastal water quality and ship operation. Compared with other commonly used methods, the proposed method has a higher recall, precision, and F1 score performance.

Water jet technology is widely used in various fields, in which the nozzle is an important element to form the jet. To solve the problem of low water jet operation efficiency of square nozzles, the internal flow channel structure of the nozzle of the key jet device is studied. Through the combination of computational fluid dynamics (CFD) and particle image velocimetry (PIV) experiments, the influence of main structural parameters such as the contraction angle and length-to-diameter ratio of the inner flow channel on the velocity and length of the constant-velocity core region is explored. Since the jet flow structure is a symmetrical structure along the axial direction, the model of the jet flow structure was built half of the model along the axial direction. The results show that a smaller length-to-diameter ratio and a smaller contraction angle of the nozzle result in better jet cohesion and lower dynamic pressure in the constant-velocity core area, which is more suitable for long-distance, low-pressure water jet operations.

Quantitative analysis of tumor vessels is of great significance for tumor staging and diagnosis. Photoacoustic imaging (PAI) has been proven to be an effective way to visualize comprehensive tumor vascular networks in three-dimensional (3D) volume, while previous studies only quantified the vessels projected in one plane. In this study, tumor vessels were segmented and quantified in a full 3D framework. It had been verified in the phantom experiments that the 3D quantification results have better accuracy than 2D. Furthermore, in vivo vessel images were quantified by 2D and 3D quantification methods respectively. And the difference between these two results is significant. In this study, complete vessel segmentation and quantification method within a 3D framework was implemented, which showed obvious advantage in the analysis accuracy of 3D photoacoustic images, and potentially improve tumor study and diagnosis.

The different creep-aging forming processes of 2524 aluminum alloy were taken as the research object, and the effects of creep-aging temperature and creep stress on the fatigue-crack propagation properties of the alloy were studied. The research results showed the following under the same sintering time of 9 h, at creep-aging temperatures of 100 °C, 130 °C, 160 °C, and 180 °C, respectively, with an increase in creep-aging temperature: the fatigue-crack propagation rate was promoted, the spacing of fatigue striations increased, and the sizes of dimples decreased while the number was enlarged; this proves that the fatigue property of the alloy was weakened. Compared with the specimens with creep deformation radii of 1000 mm and 1500 mm, the creep deformation stress was the smallest when the forming radius was 1800 mm, with a higher threshold value of fatigue-crack growth in the near-threshold region of fatigue-crack propagation (ΔK ≤ 8 MPa·m1/2). Under the same fatigue cycle, the specimens under the action of larger creep stress endured a longer fatigue stable-propagation time and a faster fracture speed. Comparing the effect of creep-aging temperature and creep stress, the creep-aging temperature plays a dominant role in the fatigue-crack propagation of creep-aged 2524 aluminum alloy.

The 2524-T3 aluminum alloy was subjected to fatigue tests under the conditions of R = 0, 3.5% NaCl corrosion solution, and the loading cycles of 106, and the S-N curve was obtained. The horizontal fatigue limit was 169 MPa, which is slightly higher than the longitudinal fatigue limit of 163 MPa. In addition, detailed microstructural analysis of the micro-morphological fatigue failure features was carried out. The influence mechanism of corrosion on the fatigue crack propagation of 2524-T3 aluminum alloy was discussed. The fatigue source characterized by cleavage and fracture mainly comes from corrosion pits, whose expansion direction is perpendicular to the principal stress direction. The stable propagation zone is characterized by strip fractures. The main feature of the fracture in the fracture zone is equiaxed dimples. The larger dimples are mixed with second-phase particles ranging in size from 1 to 5 μm. There is almost a one-to-one correspondence between the dimples and the second-phase particles. The fracture mechanism of 2524 alloy at this stage is transformed into a micro-holes connection mechanism, and the nucleation of micropores is mainly derived from the second-phase particles.

Electric field-assisted sintering has ubiquitous merits over conventional sintering technology for the fabrication of difficult-to-deform materials. To investigate the effect of sintering pressure and temperature on the densification of Inconel 718 superalloy, a numerical simulation model was established based on the Fleck-Kuhn-McMeeking (FKM) and Gurson-Tvergaard-Needleman (GTN) models, which covers a wide range of porosity. At a sintering pressure below 50 MPa or a sintering temperature below 950 °C, the average porosity of the sintered superalloy is over 0.17 with low densification. Under a pressure above 110 MPa and a temperature above 1250 °C, the sintered superalloy quickly completes densification and enters the plastic yield stage, making it difficult to control the sintering process. When the pressure is above 70 MPa while the temperature exceeds 1150 °C, the average porosity is 0.11, with little fall when the pressure or temperature rises. The experimental results indicated that the relative density of the sintered superalloy under 70 MPa and 1150 °C is 94.46%, and the proportion of the grain size below 10 μm is 73%. In addition, the yield strength of the sintered sample is 512 MPa, the compressive strength comes to 1260 MPa when the strain is over 0.8, and the microhardness is 395 Hv, demonstrating a better mechanical property than the conventional superalloy.

A 3D photoacoustic computed tomography (3D-PACT) system based on full-view illumination and ultrasound detection was developed and applied to 3D photoacoustic imaging of several phantoms. The system utilized an optics cage design to achieve full-view uniform laser illumination and completed 3D scanning with the rotation of a dual-element transducer (5 MHz) and the vertical motion of imaging target, which obtains the best solution in the mutual restriction relation between cost and performance. The 3D-PACT system exhibits a spatial resolution on the order of 300 μm, and the imaging area can be up to 52 mm in diameter. The transducers used in the system provides tomography imaging with large fields of view. In addition, the coplanar uniform illumination and acoustic detection configuration based on a quartz bowl greatly enhances the efficiency of laser illumination and signal detection, making it available for use on samples with irregular surfaces. Performance testing and 3D photoacoustic experiments on various phantoms verify that the system can perform 3D photoacoustic imaging on targets with complex surfaces or large sizes. In future, efforts will be made to achieve full-body 3D tomography of small animals and a multimodal 3D imaging system.

To ensure the quality and reliability of polymer lithium-ion battery (PLB), automatic blister defect detection instead of manual detection is developed in the production of PLB cell sheets. A convolutional neural network (CNN) based detection method is proposed to detect blister in cell sheets employing cell sheet images. An improved architecture for dense block and a learning method based on optimization of learning rate are discussed. The proposed method was superior to other machine learning based methods when the classification performance and confusion matrix were compared in experiments. The proposed CNN method had the best defect detection performance and real-time performance for industry field application.

The fatigue performance of high-strength Al-Cu-Mg alloys is generally influenced by the process of creep age formation when applied to acquire higher strength. The results show that creep aging accelerates the precipitation process, leading to a more uniform precipitation of strengthening phases in grains, as well as narrowed precipitation-free zones (PFZ). Compared with the artificially aged alloy, the yield strength and hardness of the creep aged alloy increased, but the fatigue resistance decreased. In the low stress intensity factor region (ΔK ≤ 7 MPa·m1/2), the fatigue crack propagation (FCP) rate was mainly affected by the characteristics of precipitates, and the fatigue resistance noticeably decreased with the increased creep time. In a 4 h creep aged alloy, the microstructure was dominated by Cu-Mg clusters and Guinier-Preston (GP) zones, while S″ phases began to precipitate in the matrix, showing better fatigue resistance. After aging for 24 h, the needle-shaped S’ phases were largely precipitated and coarsened, which changed the mode of dislocation slip, reduced the reversibility of slip, and accelerated the accumulation of fatigue damage. In stable and rapid crack propagation regions, the influence of precipitates on the FCP rate was negligible.