Explore the vast range of titles available.

Metal–polymer direct joining helps meeting the growing demand for lightweight design in industry. Injection molded direct joining (IMDJ) is one of the promising metal–polymer direct joining methods. It needs firstly metal surface treatment and then injection insert molding. In this study, we proposed a simple, economical, and eco-friendly surface treatment, named as hot water treatment (HWT) for IMDJ. HWT produces complexed plate-like nanostructures on the aluminum surface. Polymer can flow into nanostructures and produce a mechanical interlocking effect by which metal and polymer achieve direct joining. The HWT treatment conditions, including water temperature and treatment time, were optimized. With the optimized condition, the highest joining strength of 22.5 MPa was obtained. An atomic force microscope (AFM) investigation on nanostructures and tensile shear tests to the joined specimens proved that the joining strength was strongly correlated with the combination of the arithmetical mean height ( Sa ) and the number of nanostructures ( Nn ). In addition, the joined specimens also achieved high cycle fatigue. Results showed that high-quality joint can be produced by treating aluminum with HWT. Compared with conventional chemical surface treatment methods, HWT is much easier to apply in practices. Wide applications in industries can be expected.

To decrease subjective interference and improve the construction efficiency of the traditional weapon and equipment index system, an index system construction method based on target detection is proposed in combination with the equipment test video data. The three-level index system of combat effectiveness of a certain type of equipment is established, and various intelligent assessment methods are proposed. Firstly, an optimaized IPSO-BP network model is proposed, in which dynamic weights are set to improve the particle search network, and adaptive learning factors are introduced to optimize the update speed. Secondly, an improved DS evidence-parallel neural network assessment method is proposed, setting multiple parallel neural networks with different parameters, and improving the angle cosine to weaken the numerical nonlinear attributes in DS evidence fusion and increase the model's assessment operation stability. Thirdly, the three types of view features corresponding to the index item images are extracted to train the base classifiers. The integrated CNN network based multi-view feature integration assessment model is constructed and the improved residual network block is introduced to optimize the network gradient. Comparison with existing evaluation methods shows that the proposed methods achieve efficient and intelligent construction and evaluation of the indicator system and enrich the evaluation of indicator data.

In high-temperature environments, the signal-to-noise ratio (SNR) of the signal measured by electromagnetic acoustic transducers (EMAT) is low, and the signal characteristics are difficult to extract, which greatly affects their application in practical industry. Aiming at this problem, this paper proposes the least mean square adaptive filtering interpolation denoising method based on variational modal decomposition (AFIV). Firstly, the high-temperature EMAT signal was decomposed by variational modal decomposition (VMD). Then the high-frequency and low-frequency noises in the signal were filtered according to the excitation center frequency. Following the wavelet threshold denoising (WTD) for the noise component after VMD decomposition was carried out. Afterward, the noise component and signal component were connected by an adaptive filtering process to achieve further noise reduction. Finally, cubic spline interpolation was used to smooth the noise reduction curve and obtain the time information. To verify the effectiveness of the proposed method, it was applied to two kinds of ultrasonic signals from 25 to 700 °C. Compared with VMD, WTD, and empirical mode decomposition denoising, the SNR was increased by 2 times. The results show that this method can better extract the effective information of echo signals and realize the online thickness measurement at high temperature.

Low excitation voltage for an electromagnetic acoustic transducer (EMAT) is necessary for the petrochemical equipment and facilities inspection, which work at high-temperatures, to avoid potential explosion. However, low excitation voltage causes low signal-to-noise ratio (SNR) signals that are difficult to extract features, especially in a high-temperature environment, which causes high noise. In this study, a denoising method called the variational wavelet ensemble empirical (VWEE) method was proposed by combining the advantages of the variational modal decomposition (VMD), wavelet threshold (WT) denoising, and ensemble empirical mode decomposition (EEMD) methods. To validate the VWEE method, EMAT signals obtained in the temperature range of 25 to 700 °C were analyzed. The results show that, compared with VMD, WT and empirical mode decomposition denoising methods, the SNR of proposed method is improved more than two times. The VWEE method dramatically improved the SNR of a high-temperature EMAT signal and enhanced the accuracy of defect echos extraction.

Efficient hybrid joining methods are required for joining metals and plastics in the automobile and airplane industries. Injection molded direct joining (IMDJ) is a direct joining technique that uses metal pretreatment and injection molding of plastic to form joints without using any additional parts. This joining technique has attracted attention from industries for its advantages of high efficiency and low cost in mass production. Blast-assisted IMDJ, an IMDJ technique that employs blasting as the metal pretreatment, has become suitable for the industry because metal pretreatment can be performed during the formation of the metal surface structure without chemicals. To satisfy industry standards, the blast-assisted IMDJ technique needs to be optimized under blasting conditions to improve joining performance. The number of parameters and their interactions make this problem difficult to solve using conventional control variable methods. We propose applying statistical and artificial intelligence analyses to address this problem. We used multiple linear regression and back propagation neural networks to analyze the experimental data. The results elucidated the relationship between the blasting conditions and joining strength. According to the machine learning predicted results, the best joining strength in blast-assisted IMDJ reached 22.3 MPa under optimized blasting conditions. This study provides new insights into similar engineering problems.

Low excitation voltage for an electromagnetic acoustic transducer (EMAT) is necessary for the petrochemical equip- ment and facilities inspection, which work at high-temperatures, to avoid potential explosion. However, low excitation voltage causes low signal-to-noise ratio (SNR) signals that are difficult to extract features, especially in a high-temper-ature environment, which causes high noise. In this study, a denoising method called the variational wavelet ensem-ble empirical (VWEE) method was proposed by combining the advantages of the variational modal decomposition (VMD), wavelet threshold (WT) denoising, and ensemble empirical mode decomposition (EEMD) methods. To validate the VWEE method, EMAT signals obtained in the temperature range of 25 to 700 ℃ were analyzed. The results show that, compared with VMD, WT and empirical mode decomposition denoising methods, the SNR of proposed method is improved more than two times. The VWEE method dramatically improved the SNR of a high-temperature EMAT signal and enhanced the accuracy of defect echos extraction.

Previous research has highlighted the impact of family environment on college students’ cyberbullying behavior, yet the role of parenting styles, particularly negative ones, remains underexplored. This study, grounded in the interpersonal acceptance-rejection theory and social information processing model, investigates how parental rejection influences cyberbullying behavior among college students through cognitive and emotional mechanisms. We surveyed 1,567 college students (620 males, 947 females; average age: 19.34 ± 1.24 years) from several universities in Shandong and Jilin provinces, China. Participants completed questionnaires assessing cyberbullying, parental rejection, empathy, and moral disengagement. The findings reveal that 456 individuals (29.1%) had engaged in at least one instance of cyberbullying behavior, including 180 males and 276 females. Subsequently, an investigation into the cyberbullying behaviors of these individuals revealed that: (1) parental rejection is a significant predictor of cyberbullying behavior; (2) empathy and moral disengagement serve as partial mediators in the relationship between parental rejection and cyberbullying; (3) both empathy and moral disengagement act as sequential mediators in this relationship. These results underscore the importance of empathy and moral disengagement in understanding the link between parental rejection and cyberbullying among college students, offering a new theoretical perspective for future interventions.

In clay-sand reservoirs, shale volume affects porosity and permeability, with porosity governing storage capacity; these properties influence reserve and productivity predictions, which directly affect reservoir and economic assessments. Estimates of porosity and shale volume from independent log-based methods may introduce coupled biases, whereas those from joint inversion better honor their interdependence. Joint inversion has traditionally relied on simplified assumptions or extra data; in contrast, recent data-driven approaches capture complex log patterns. However, purely data-driven methods suffer from feature-target shifts and cannot enforce inter-target dependencies. To address these limitations, a two-stage deep learning framework combining self-supervised log modeling with core-calibrated low-rank adaptation (CCLoRA) is proposed for joint porosity and shale volume prediction. First, a Conditional Score-based Diffusion Imputation (CSDI) model is self-supervised on synthetic logs generated from empirical formulas. This enables learning of plausible log sequence structures and confers partial robustness to feature-target shifts without extensive labeled data. Second, core-scale petrophysical relationships are transferred to the log scale through well-specific feature replacement using CCLoRA. This corrects residual feature-target shifts and enforces inter-target dependencies between the two parameters with minimal fine-tuning cost. Experiments on well-consolidated sandstones show the full pipeline outperforms multiple deep learning baselines, delivering accurate and physically consistent estimates.