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In response to the real-time imaging detection requirements of structural defects in the R region of rib-stiffened wing skin, a defect detection algorithm based on phased-array ultrasonic imaging for wing skin with stiffener is proposed. We select the full-matrix–full-focusing algorithm with the best imaging quality as the prototype for the required detection algorithm. To address the problem of poor real-time performance of the algorithm, a sparsity-based full-focusing algorithm with symmetry redundancy imaging mode is proposed. To address noise artifacts, an adaptive beamforming method and an equal-acoustic-path echo dynamic removal scheme are proposed to adaptively suppress noise artifacts. Finally, within 0.5 s of imaging time, the algorithm achieves a detection sensitivity of 1 mm and a resolution of 0.5 mm within a single-frame imaging range of 30 mm × 30 mm. The defect detection algorithm proposed in this paper combines phased-array ultrasonic technology and post-processing imaging technology to improve the real-time performance and noise artifact suppression of ultrasound imaging algorithms based on engineering applications. Compared with traditional single-element ultrasonic detection technology, phased-array detection technology based on post-processing algorithms has better defect detection and imaging characterization performance and is suitable for R-region structural detection scenarios.

Background Pancreatic cancer (PC) is an extremely malignant tumor with low survival rate. Effective biomarkers and therapeutic targets for PC are lacking. The roles of circular RNAs (circRNAs) in cancers have been explored in various studies, however more work is needed to understand the functional roles of specific circRNAs. In this study, we explore the specific role and mechanism of circ_0035435 (termed circCGNL1) in PC. Methods qRT-PCR analysis was performed to detect circCGNL1 expression, indicating circCGNL1 had low expression in PC cells and tissues. The function of circCGNL1 in PC progression was examined both in vitro and in vivo. circCGNL1-interacting proteins were identified by performing RNA pulldown, co-immunoprecipitation, GST-pulldown, and dual-luciferase reporter assays. Results Overexpressing circCGNL1 inhibited PC proliferation via promoting apoptosis. CircCGNL1 interacted with phosphatase nudix hydrolase 4 (NUDT4) to promote histone deacetylase 4 (HDAC4) dephosphorylation and subsequent HDAC4 nuclear translocation. Intranuclear HDAC4 mediated RUNX Family Transcription Factor 2 (RUNX2) deacetylation and thereby accelerating RUNX2 degradation. The transcription factor, RUNX2, inhibited guanidinoacetate N-methyltransferase (GAMT) expression. GAMT was further verified to induce PC cell apoptosis via AMPK–AKT–Bad signaling pathway. Conclusions We discovered that circCGNL1 can interact with NUDT4 to enhance NUDT4-dependent HDAC4 dephosphorylation, subsequently activating HDAC4–RUNX2–GAMT-mediated apoptosis to suppress PC cell growth. These findings suggest new therapeutic targets for PC.

Cylindrical components are parts with curved surfaces, and their high-precision defect testing is of great significance to industrial production. This paper proposes a noncontact internal defect imaging method for cylindrical components, and an automatic photoacoustic testing platform is built. A synthetic aperture focusing technology in the polar coordinate system based on laser ultrasonic (LU-pSAFT) is established, and the relationship between the imaging quality and position of discrete points is analyzed. In order to verify the validity of this method, small holes of Φ0.5 mm in the aluminum alloy rod are tested. During the imaging process, since a variety of waveforms can be excited by the pulsed laser synchronously, the masked longitudinal waves reflected by small holes need to be filtered and windowed to achieve high-quality imaging. In addition, the influence of ultrasonic beam angle and signal array spacing on imaging quality is analyzed. The results show that the method can accurately present the outline of the small hole, the circumferential resolution of the small hole is less than 1° and the dimensional accuracy and position error are less than 0.1 mm.

In this study, to investigate the problem of contact fatigue and the damage mechanism of an AD180 high-carbon semi-steel roll, rolling contact fatigue tests were conducted using specimens cut from the periphery of a roll ring. These specimens were characterized under different contact stresses using SEM, a profile system, an optical microscope, and a Vickers hardness tester. The results indicates that the main forms of fatigue damage of an AD180 high-carbon semi-steel roll are peeling, pitting corrosion, and plowing. Moreover, the surface of the roll exhibits delamination and plastic deformation characteristics under high contact stress. Meanwhile, the size and depth of peeling, as well as the amount of pitting corrosion, increase with the contact stress. Peeling is mainly caused by a crack that originates at the edge of the specimen surface and propagates along the pearlite structure and the interface between pearlite and cementite. High contact stress can lead to an increase in the crack propagation depth and angle, resulting in the formation of larger peeling. Under cyclic loading, the near-surface microstructure of the specimen hardens due to grain refinement and dislocation strengthening, and the depth of the hardened layer increases with the increase in contact stress. When the contact stress reaches 1400 MPa, the near surface structure of the specimen changes from pearlite to troostite.

Coral reefs in Sanya Bay have been degrading in recent decades under climate change and human activities. To identify physiological changes of scleractinian corals and corresponding influencing factors, aquatic environmental factors and physiological parameters of Pocillopora damicornis, Porites pukoensis and their symbiotic zooxanthellae were examined in four Sanya Bay coral reef areas in December 2020 (winter) and July 2021 (summer). The density and chlorophyll a+c2 content of the symbiotic zooxanthellae were significantly high in winter and low in summer. Superoxide dismutase and caspase3 activities of corals and zooxanthellae were high in summer and low in winter, whereas catalase activity showed the opposite pattern. The variations were consistent for both coral symbionts. Water temperature and salinity were the main factors affecting the physiological variations of corals. Compared with winter, the high temperature/low salinity aquatic environment in summer reduced the density and chlorophyll a+c2 content of zooxanthellae, resulting in high superoxide dismutase and caspase3 activities in the corals and zooxanthellae. In addition, turbidity was an important factor affecting the physiological characteristics of coral–zooxanthellae symbionts among the four coral reef areas. Our results have important implications for understanding the changes in coral reef communities in Sanya Bay and coral reef protection.

Financial Time Series Forecasting (TSF) remains a critical challenge in Artificial Intelligence (AI) due to the inherent complexity of financial data, characterized by strong non-linearity, dynamic non-stationarity, and multi-factor coupling. To address the performance limitations of Spiking Neural Networks (SNNs) caused by hyperparameter sensitivity, this study proposes an SNN model optimized by an Improved Cuckoo Search (ICS) algorithm (termed ICS-SNN). The ICS algorithm enhances global search capability through piecewise-mapping-based population initialization and introduces a dynamic discovery probability mechanism that adaptively increases with iteration rounds, thereby balancing exploration and exploitation. Applied to futures market price difference prediction, experimental results demonstrate that ICS-SNN achieves reductions of 13.82% in MAE, 21.27% in MSE, and 15.21% in MAPE, while improving the coefficient of determination (R2) from 0.9790 to 0.9822, compared to the baseline SNN. Furthermore, ICS-SNN significantly outperforms mainstream models such as Long Short-Term Memory (LSTM) and Backpropagation (BP) networks, reducing prediction errors by 10.8% (MAE) and 34.9% (MSE), respectively, without compromising computational efficiency. This work highlights that ICS-SNN provides a biologically plausible and computationally efficient framework for complex financial TSF, bridging the gap between neuromorphic principles and real-world financial analytics. The proposed method not only reduces manual intervention in hyperparameter tuning but also offers a scalable solution for high-frequency trading and multi-modal data fusion in future research.

Arbitrage spread prediction can provide valuable insights into the identification of arbitrage signals and assessing associated risks in algorithmic trading. However, achieving precise forecasts by increasing model complexity remains a challenging task. Moreover, uncertainty in the development and maintenance of model often results in extremely unstable returns. To address these challenges, we propose a K-fold cross-search algorithm-optimized LSTM (KCS-LSTM) network for arbitrage spread prediction. The KCS heuristic algorithm incorporates an iterative updating mechanism of the search space with intervals as the basic unit into the traditional ant colony optimization. It optimized the hyperparameters of the LSTM model with a modified fitness function to automatically adapt to various data sets, thereby simplified and enhanced the efficiency of model development. The KCS-LSTM network was validated using real spread data of rebar and hot-rolled coil from the past three years. The results demonstrate that the proposed model outperforms several common models on sMAPE by improving up to 12.6% to 72.4%. The KCS-LSTM network is shown to be competitive in predicting arbitrage spreads compared to complex neural network models.

Electronic health record (EHR) data have emerged as an important resource for population health and clinical research. There have been significant efforts to leverage EHR data for research; however, given data security concerns and the complexity of the data, EHR data are frequently difficult to access and use for clinical studies. We describe the development of a Clinical Research Datamart (CRDM) that was developed to provide well-curated and easily accessible EHR data to Duke University investigators. The CRDM was designed to (1) contain most of the patient-level data elements needed for research studies; (2) be directly accessible by individuals conducting statistical analyses (including Biostatistics, Epidemiology, and Research Design (BERD) core members); (3) be queried via a code-based system to promote reproducibility and consistency across studies; and (4) utilize a secure protected analytic workspace in which sensitive EHR data can be stored and analyzed. The CRDM utilizes data transformed for the PCORnet data network, and was augmented with additional data tables containing site-specific data elements to provide additional contextual information. We provide descriptions of ideal use cases and discuss dissemination and evaluation methods, including future work to expand the user base and track the use and impact of this data resource. The CRDM utilizes resources developed as part of the Clinical and Translational Science Awards (CTSAs) program and could be replicated by other institutions with CTSAs.

Long short-term memory (LSTM) networks, widely used for financial time series forecasting, face challenges in arbitrage spread prediction, especially in hyperparameter tuning for large datasets. These issues affect model complexity and adaptability to market dynamics. Existing heuristic algorithms for LSTM often struggle to capture the complex dynamics of futures spread data, limiting prediction accuracy. We propose an integrated Cuckoo and Zebra Algorithms-optimised LSTM (ICS-LSTM) network for arbitrage spread prediction. This method replaces the Lévy flight in the Cuckoo algorithm with the Zebra algorithm search, improving convergence speed and solution optimization. Experimental results showed a mean absolute percentage error (MAPE) of 0.011, mean square error (MSE) of 3.326, mean absolute error (MAE) of 1.267, and coefficient of determination (R2) of 0.996. The proposed model improved performance by reducing MAPE by 8.3–50.0%, MSE by 10.2–77.8%, and MAE by 9.3–63.0% compared to existing methods. These improvements translate to more accurate spread predictions, enhancing arbitrage opportunities and trading strategy profitability.

Metallic cylinders are widely used in various fields of industrial production, and the automatic detection of surface microcracks is of great significance to the subsequent grinding process. In this paper, laser-excited surface acoustic waves (SAW) are used to detect surface microcracks. Due to the dispersion of SAWs on the cylinder surface, the SAWs exhibit different polarities at different positions. In order to improve the consistency of signals and the accuracy of the modeling, the angle at which the polarity is completely reversed is selected as the detection point. A laser ultrasonic automatic detection system is established to obtain signals, and the B-scan image is drawn to determine the location of the microcrack. By comparing the time–frequency diagrams of the reflected SAWs and transmitted SAWs, the transmitted wave is chosen to establish the microcrack depth prediction model. In addition, according to the trajectory of the grinding wheel, a prediction model based on the absolute depth of the microcracks is established, and the influence of the orientation of the microcracks on the signal energy is considered. The method proposed in this paper can provide a reference for the rapid grinding of microcracks on the surface of metallic cylinders; it has the characteristics of visualization and high efficiency, and overcomes the shortcomings of the currently used eddy current testing that provides information on the depth of microcracks with difficulty.