Background: PFO (Patent foramen ovale) is a common defect that affects about 25% of the population. Although its presence is asymptomatic in the majority of the cases, the remaining part becomes overt with different symptoms, including cryptogenic stroke. The PFO closure procedure is widely available to date with the most used Amplatzer PFO Occluder, also in complex anatomy, but the performance of another device, the GORE Septal Occluder (GSO), has not been completely explored with regard to different septal anatomies. Methods: From March 2012 to June 2020, 118 consecutive patients with an indication for PFO closure were treated using the GSO system and included in a prospective analysis and followed. After 12 months, every patient underwent transcranial Doppler to evaluate the effectiveness of treatment. Results: of 111 patients evaluated, 107 showed effective PFO closure (96,4%) and 4 showed a residual shunt (3,6%). To better evaluate the device performance, the overall population was sorted into 2 clusters based on the echocardiographic characteristics. The main difference between groups was for PFO width (4,85 ± 1,8 vs 2,9 ± 1 mm, p <0,001) and PFO tunnel length (12,6 ± 3,8 vs 7,2 ± 2 respectively, p <0,001), allowing identification of complex anatomy and simple anatomy, respectively. Regardless of the aforementioned cluster, the GSO performance to reach an effective closure was independent of anatomy type and the chosen device size. Conclusion: GSO device showed efficacy and safety at 1-year follow-up in patients with at least 1 anatomical factor of complexity of PFO, irrespectively from the level of complexity itself.

This research investigates the fatigue behaviour and fracture mechanics of high-performance concrete (HPC), including various compositions such as HPC with basalt aggregates (HPC-B), HPC with gravel (HPC-G), and high-strength coarse mortar (CM) static and cyclic tensile loading within the special priority program SPP 2020. The study aims to integrate fracture mechanics into structural analysis to enhance design guidelines for slender cross-sections and safety-related high-performance structural components. The experimental investigations reveal HPC-B's re-markable superiority, displaying higher compressive strength, modulus of elasticity, and tensile strength compared to HPC-G and CM. A modified disk-shaped compact tension (MDCT) based on ASTM standards, aided by digital image correlation (DIC) unveils fracture behaviour, empha-sizing fracture energy as a crucial parameter. HPC-B exhibits improved crack resistance and notch sensitivity reduction attributed to crushed basalt aggregates and enhanced interfacial transition zone (ITZ). The research scrutinizes factors like material characterization, aggregate morphology, stress levels, and displacement rate on crack formation. High-cycle fatigue tests show HPC-B's superior performance, and post-fatigue analysis reveals enhanced residual fracture toughness attributed to nano-level structural changes, stress redistribution and aggregate-matrix interac-tion. 3D image analysis via Computed Tomography (CT) scans captures mesostructural crack propagation and provide quantitative insights. This study advances the understanding of HPC fatigue behaviour, emphasizing the interplay of aggregate types, morphologies, and their dy-namic response to cyclic loading, offering valuable insights for optimizing design guidelines and fostering innovation in structural engineering.

Plastic pollution is a pressing environmental challenge, necessitating the development of biodegradable alternatives like polyhydroxybutyrate (PHB). This study focuses on optimizing PHB production by Neobacillus niacinii GS1, a bacterium isolated from a municipal dumping site. By utilizing agricultural residues such as corn flour, wheat bran, and peptone as substrates, we aimed to establish an eco-friendly method for biopolymer production, contributing to sustainable waste management and bioplastic innovation. The bacterium was identified using morphological, biochemical, and molecular techniques. The optimization process involved adjusting variables such as inoculum age, inoculum size, incubation time, agitation rate, incubation temperature, pH of the medium, carbon sources and nitrogen sources. Response surface methodology (RSM) was employed to identify optimal conditions, with the highest PHB yield of 61.1% achieved under specific conditions: 37°C, pH 7, and an agitation rate of 150 rpm. These findings underscore the potential of Neobacillus niacinii GS1 in converting agro-industrial residues into valuable biopolymers, promoting sustainable bioplastic production, and advancing waste valorization efforts through the use of eco-friendly materials.

The microscopic pore structure of tight sandstone reservoir significantly influences the characteris-tics of CO2 flooding after water flooding. The research was conducted using various techniques such as casting thin sections, high pressure mercury injection, scanning electron microscopy, Nu-clear magnetic resonance (NMR) testing, and a self-designed high-temperatures and high-pressures microscopic visualization displacement system. Three type cores with different pore structures were selected for the flooding experiments and the microscopic visualization displace-ment experiments, including CO2 immiscible flooding, near-miscible flooding, and miscible flood-ing after the conventional water flooding. The characteristics of CO2 flooding and the residual oil distribution after water flooding were quantitatively analyzed and evaluated. The results show that: (1) During the water flooding process, the oil produced from type I and type III samples mainly comes from large and some medium pores. Oil utilization of all pores is significant for type II samples. The physical properties and pore types have a greater impact on water flooding. Type I and II samples are more suitable for near-miscible flooding after water flooding. Type III samples are more suitable for miscible flooding after water flooding. (2) In the CO2 flooding, oil recovery increases gradually with increasing pressure for all three types of samples. Type II core samples have the highest recovery. Before miscibility, the oil recovered from type I and type II samples was primarily from large pores, however, the oil recovery mainly comes from medium pores when reaching miscibility. As for the type III samples, the oil produced in the immiscible state mainly comes from large and medium pores, and the enhanced oil recovery mainly comes from medium and small pores after reaching the near-miscible phase. (3) It can be seen from the microscopic re-sidual oil distribution that oil recovery will increase as the petrophysical properties of the rock model improve. The oil recovery of near-miscible flooding after water flooding regarding the type II model is up to 68.11%. The oil recovery of miscible flooding after water flooding about type III model is the highest at 74.57%. With increasing pressure, the proportion of flake residual oil grad-ually decreases, while the proportion of droplet-like and film-like residual oil gradually increases. Type II samples have a relatively large percentage of reticulated residual oil in the near-miscible stage.

Thermoplastic composites are continuously replacing thermosetting composites in lightweight structures. However, the accomplished work on the fatigue behavior of thermoplastics is quite limited. In the present work, we propose a numerical modeling approach for simulating fatigue delamination growth and predicting the residual tensile strength of quasi-isotropic TC 1225 LM PAEK thermoplastic coupons. The approach is supported and validated by tension and fatigue (non-interrupted and interrupted) tests. Fatigue delamination growth is simulated using a mixed-mode fatigue crack growth model, which is based on the cohesive zone modeling method. All interfaces between the thermoplastic plies were modelled using cohesive elements. Quasi-static tension analyses on pristine and fatigued coupons were performed using a progressive damage model implementing a set of Hashin-type strain-based failure criteria and a damage mechanics-based material property degradation module. Using the fatigue model, delamination growth was predicted as a function of number of cycles at all interfaces. The results agree well with C-scan images taken on fatigued coupons during interruptions of fatigue tests. An unequal and unsymmetric delamination growth was predicted due to the quasi-isotropic lay-up. Moreover, the combined models capture the decrease in the residual tensile strength of the coupons. In the quasi-static tension analysis of the fatigued coupons, the driving failure mechanisms are the fast propagation of the pre-existing delamination and the severe matrix cracking.

The main purpose of this work was to tests of form and size of fatigue wear of the top layer of oil gear pump wheel (3PW-BPF-24). The object of tests was operated during a determined number of hours at strictly controlled operational and environmental parameters. A microscope of focal differentiation by Alicona InfiniteFocus G4 (with Focus-Variation optical measuring technology) was used for an identification, measurement and description of noticed superficial defects, which resulted from the fatigue process of the top layer and an earlier thermochemical treatment. The work was finished with an analysis of obtained results (based on the analysis of 3D scans) and conclusions concerning about optimization of the thermal treatment. Taking into consideration the presented results (suggestions) in the technology of improving strength properties of gear wheels (of this type oil pumps) may render it possible to obtain an appropriate fatigue endurance of the top layer, i.e. a minimization of observed superficial defects.

In this paper, we concentrate on the study of the properties of residual Tsallis entropy for order statistics. Order statistics have an important role in reliability structural engineering for example for modelling lifetimes of series and parallel systems. The residual Tsallis entropy of ith order statistic from a continuous distribution function and its deviation from the residual Tsallis entropy of ith order statistics from a uniform distribution is investigated. In a mathematical framework, a method to express the residual Tsallis entropy of the ith order statistic from a continuous distribution in terms of the residual Tsallis entropy of the ith order statistic from a uniform distribution is provided. This approach may provide insight into the behavior and properties of the residual Tsallis entropy for order statistics. Further, we study the monotonicity properties of the residual Tsallis entropy of order statistics under dierent conditions. By studying these properties, deeper understanding of the relationship between the position of order statistics and the resulting residual Tsallis entropy is gained.

In the global fight against malaria, standard vector control methods such as indoor residual spraying (IRS) and insecticide-treated nets (ITNs) are intended to protect inside residential structures and sleeping spaces. However, these methods can still leave individuals vulnerable to residual transmission from vectors that they may be exposed to outdoors. Nchelenge District in northern Zambia experiences high persistent malaria transmission even with ITNs and IRS in place. However, very few studies have examined outdoor vector activity. To assess diversity and abundance of outdoor foraging female anopheline mosquitoes, CDC light traps were used as proxy measures for mosquito host-seeking, set in three outdoor trapping schemes randomly assigned on different nights: 1) locations where people congregate at night outside of the house within the peri-domestic space, 2) animal pens or shelters, and 3) high human-trafficked areas, such as paths to latrines, where traps were baited with BG-Lure®. 1,087 total female anophelines were collected over a total of 74 trap nights. Anopheles funestus s.s. comprised the majority of the collection (86%), with An. gambiae s.s. (2%) and a highly diverse sampling of other anophelines (12%) making up the remainder. Plasmodium falciparum parasites were only detected in An. funestus (1%). No significant difference in species diversity or female anopheline abundance was detected between trapping schemes. Outdoor foraging anopheline mosquitoes, including a number of infectious An. funestus, may partially explain the difficulty of controlling malaria transmission in Nchelenge District where vector control is only targeted indoors. BG-Lure® shows some promise as an alternative to human-baited landing catch collections in this resource-poor setting.

Infrared and visible image fusion technologies are used to characterize the same scene by diverse modalities. However, most existing deep learning-based fusion methods are designed as symmetric networks, which ignore the differences between modal images and lead to the source image information loss during feature extraction. In this paper, we propose a new fusion framework for the different characteristics of infrared and visible images. Specifically, we design a dual-stream asymmetric network with two different feature extraction networks to extract infrared and visible feature maps respectively. The transformer architecture is introduced in the infrared feature extraction branch, which can force the network to focus on the local features of infrared images while still obtaining their contextual information. And the visible feature extraction branch uses residual dense blocks to fully extract the rich background and texture detail information of visible images. In this way, it can provide better infrared targets and visible details for the fused image. Experimental results on multiple datasets indicate that DSA-Net outperforms state-of-the-art methods in both qualitative and quantitative evaluations. In addition, we also apply the fusion results to the target detection task, which indirectly demonstrates the fusion performances of our method.

Compressed Sensing (CS) MRI has shown great potential in enhancing time efficiency. Deep learning techniques, specifically Generative Adversarial Networks (GANs), have emerged as potent tools for speedy CS-MRI reconstruction. Yet, as the complexity of deep learning recon-struction models increases, this can lead to prolonged reconstruction time and challenges in achieving convergence. In this study we present a novel GAN-based model that delivers superior performance without escalating model complexity. Our generator module, built on the U-net architecture, incorporates dilated residual (DR) networks, thus expanding the network's receptive field without increasing parameters or computational load. At every step of the downsampling path, this revamped generator module includes a DR network, with the dilation rates adjusted according to the depth of the network layer. Moreover, we have introduced a channel attention mechanism (CAM) to distinguish between channels and reduce background noise, thereby fo-cusing on key information. This mechanism adeptly combines global maximum and average pooling approaches to refine channel attention. We conducted comprehensive experiments with the designed model using public domain MRI datasets of the human brain. Ablation studies af-firmed the efficacy of the modified modules within the network. Compared to other relevant models, our proposed model exhibits exceptional performance, achieving not only excellent sta-bility but also outperforming other networks in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The model presents a promising pathway for enhancing the efficiency and quality of CS-MRI reconstruction.

Temporal downscaling of gridded geophysical data is essential for improving climate models, weather forecasting, and environmental assessments. However, existing methods often could not accurately capture multi-scale temporal features, affecting their accuracy and reliability. To address this issue, we introduce an Enhanced Residual U-Net architecture for temporal downscaling. The architecture, which incorporates residual blocks, allows for deeper network structures without the risk of overfitting or vanishing gradients, thus capturing more complex temporal dependencies. The U-Net design inherently could capture multi-scale features, making it ideal for simulating various temporal dynamics. Moreover, we implement a flow regularization technique with advection loss to ensure that the model adheres to physical laws governing geophysical fields. Our experimental results across various variables within the ERA5 dataset demonstrate an improvement in downscaling accuracy, outperforming other methods.

Rigid pavements at military airfields experience surface deterioration within 6-18 months of construction. The cause of this degradation is mainly due to combined exposure to repeated heat shocks from jet engine exhaust and spilled aviation oils (hydrocarbons). Surface degradation occurs in the form of disintegration of aggregates and cement paste into small pieces that pose severe risks of physical injury to maintenance crews or damage to an aircraft engine. Since coarse aggregates typically occupy 60-80% of the concrete volume, aggregates' thermal properties and microstructure should play a crucial role in the degrading mechanism. At high temperatures, concrete with lightweight aggregates is reported to have better performance compared to concrete with normal-weight aggregate. Thus, the present study carried out a detailed investigation of the mechanical and thermal performance of lightweight aggregate concrete exposed to the combined effects of high temperature and hydrocarbon oils simultaneously. To identify the resistance of different concrete with various lightweight coarse aggregates, pumice, perlite, lytag (sintered fly ash), and crushed brick were used as lightweight coarse aggregates in concrete. Also, basalt aggregate concrete was used as a reference. After cyclic exposures, all specimens were tested for residual mechanical, thermal, chemical, and microstructural properties. Overall, concrete with crushed brick aggregate and LYTAG used in this study showed superior resistance to the simulated airfield conditions.

This paper discusses the theoretical and experimental correlations between fatigue and static strength statistical distributions. We use a two-parameter residual strength model that obeys the qualitative Strenght-Life Equal-Rank (SLERA) assumption for guidance. The modeling approach consists of recovering the model's parameters by best fitting the constant amplitude (CA) fatigue data at a given stress ratio, R, and the experimental Weibull parameters of the static strength distribution function. Once the model's parameters are fixed, its capabilities, potential, and limits are discussed by comparing its predictions with residual strength and fatigue data obtained at different stress ratios, R. Moreover, from a preliminary analysis, the theoretical extension of the model's capabilities to variable amplitude loadings is conceptualized. The application of Miner's rule is also discussed and compared with a new damage rule to analyze the fatigue responses under variable amplitude loadings.

Acacia dealbata Link, named Mimosa in Portugal, is an invasive hardwood species with potential for construction use, but research is limited. The available stock of small-diameter juvenile wood (sjw) logs can help reduce this gap, but tangential cracking at log ends challenges fastener connections. This study evaluated different treatments to control and reduce end-face cracking in sjw logs during air drying, an economical and environmentally friendly procedure. Extreme two-thirds of sixteen Mimosa logs were subjected to two treatments: one with longitudinal kerfs 15 mm deep along the length (2 and 3 kerfs) and the other with a hollow in the centre up to half the length (16 mm and 30 mm diameters). Over 219 days of air drying and compared with the central part, kerfing treatments significantly reduced outer wood tangential cracking (p &lt; 0.001), with the 3-kerf also reducing crack numbers (p &lt; 0.05) but increasing significantly cracks near the pith (p &lt; 0.01). The 30 mm central hollow significantly reduced central perforation cracking (p &lt; 0.05). Results suggest that a combined treatment approach could help mitigate cracking, enhancing the suitability of wild Mimosa logs for construction use.

Image stitching is a crucial aspect of image processing. However, factors like perspective and environment often lead to irregular shapes in stitched images. Cropping or completion methods typically result in substantial loss of information. This paper proposes a method for rectifying irregularly images into rectangles using deformable meshes and residual networks. The method utilizes a convolutional neural network to quantify rigid structures of images. Choosing the most suitable mesh structure based on the extraction results, offering options such as triangular, rectangular, and hexagonal. Subsequently, the irregularly image, predefined mesh structure, and predicted mesh structure are input into a wide residual neural network for regression. The loss function comprises local and global, aimed at minimizing the loss of image information within the mesh and global target. This method not only significantly reduces information loss during rectification but also adapting to different images with various rigid structures. Validation on the DIR-D dataset shows this method outperforms state-of-the-art methods in image rectification.

The paper presents the results of experimental studies and mathematical modeling of the effect of stress accumulation in thermosetting epoxy polymer "hot" curing on anhydride hardener. Stress accumulation occurs under cyclic thermomechanical action in a locked rod, similar effects are observed, for example, in thermoplastic elements subjected to the combined effect of mechanical loads and variable temperatures. Experimental results have shown that under cyclic thermomechanical loading during exploitation it is possible to accumulate residual stresses in the thermosetting polymer. A multi-element structural mathematical model with memory for thermomechanical load, which allows to consider the accumulation of residual thermal stresses, has been proposed. The results of modeling and experiment are compared. Based on the results, corrections are made in the approach to the application of this model to thermosetting polymers such as the epoxy resin under study. Considering the corrections, a good agreement (the difference is from 1 to 6%) with the results of experimental determination of stresses under complex cyclic thermomechanical load was obtained.

The paper shows that it is possible to obtain reliable information on the dependence of radial distribution of longitudinal birefringence in glass cylindrical elements with radial distribution of refractive index (GRIN lenses) on the basis of transverse polarization tomography data of residual stresses. This does not require complicated procedures of sample preparation, as is necessary in the case of longitudinal translucency measurements. The approach developed was verified in the experiments with a set of different GRIN lenses formed with ion exchange technique, and the closeness of the data obtained from transversal and conventional longitudinal transmission measurements was demonstrated.

With the advancement of technology, signal modulation types are becoming increasingly diverse and complex. The phenomenon of signal time-frequency overlap during transmission poses significant challenges for the classification and recognition of mixed signals, including poor recognition capabilities and low generality. This paper presents a recognition model for fine-grained analysis of mixed signal characteristics, proposing a Geometry Coordinate attention mechanism and introducing a low-rank bilinear pooling module to more effectively extract signal features for classification. The model employs a residual neural network as its backbone architecture and utilizes the Geometry Coordinate attention mechanism for time-frequency weighted analysis based on information geometry theory. This analysis targets multiple-scale features within the architecture, producing time-frequency weighted features of the signal. These weighted features are further analyzed through a low-rank bilinear pooling module, combined with the backbone features, to achieve fine-grained feature fusion. This results in a fused feature vector for mixed signal classification. Experiments were conducted on a simulated dataset comprising 39,600 mixed signal time-frequency plots. The model was benchmarked against a baseline using a residual neural network. The experimental outcomes demonstrated an improvement of 9% in the exact match ratio and 5% in the Hamming score. These results indicate that the proposed model significantly enhances the recognition capability and generalizability of mixed signal classification.

This study presents the Distilled MixUp Squeeze ResNet (DMSResNet), an optimized variant of the Residual Network architecture designed for high accuracy and computational efficiency. By integrating Squeeze-and-Excitation blocks, MixUp data augmentation, and knowledge distillation, it achieves competitive performance within strict parameter constraints and limited training resources. Evaluated on the CIFAR-10 dataset, the model attains a 96.56% accuracy with only 4.3 million total parameters. The results demonstrate that optimization of established architectures can yield performance comparable to newer models, especially in resource-limited scenarios. This work contributes to the development of efficient deep learning models for applications in constrained computational environments.

Due to its economic benefits and the ability to reduce carbon emissions on a large scale, the CCUS-EOR project has been widely promoted and implemented globally. By using carbon dioxide flooding, oil water transition zone reservoirs that were once considered worthless may have new potential for development because of its rich geological reserves and high quality carbon sequestration conditions. Unlike water flooded layer, the oil in the TZ is unsaturated rather than residual oil after water flooding, which would lead to special oil-water seepage law of initial oil saturation. It is necessary to clarify special oil-water seepage law of oil-water transition zone to evaluate its production & storage potential. In this paper, we present a series of experiments to characterize the relationship between residual oil saturation and initial oil saturation. Cores in four different permeability and oil in four different viscosity are used to simulate different reservoir conditions, and that CO2 immiscible flooding experiments for each cores permeability and oil viscosity are performed with four different initial oil saturation. A total of 36 sets of CO2 flooding experiment data are used to draw the initial oil saturation & residual oil saturation chart. The results show that the residual oil saturation depends on the initial oil saturation rather than constant under certain conditions of core and oil. Under the condition of low permeability and high oil viscosity, the influence of initial oil saturation on residual oil saturation is relatively strong. The initial oil saturation & residual oil saturation chart can accurately calculate the residual oil saturation, initial water cut and final recovery based on the core permeability, oil viscosity and the initial oil saturation which is the first time to put forward this kind of chart and of the great significance to evaluate the recovery potential of the TZ.

Recently, there has been growing interest in alternative measures of uncertainty, including cumulative residual entropy. In this paper, we consider a mixed system consisting of n components, assuming that all components are operational at time t. We use the system signature to calculate the cumulative residual entropy of the mixed system’s remaining lifetime. This measure is a useful criterion for assessing the predictability of a system’s lifetime. We study several results related to the cumulative residual entropy of mixed systems, including expressions, limits, and order properties. These results shed light on the behavior of the measure and provide insights into the predictability of mixed systems. In addition, we propose a criterion for selecting a preferred system based on the relative residual cumulative entropy. This criterion is closely related to the parallel system and provides a practical way to choose the best system configuration. Overall, the present study of cumulative residual entropy and the proposed selection criterion provide valuable insights into the predictability of mixed systems and can be applied in various fields.

Single image deraining (SID) has shown its importance in many advanced computer vision tasks. Though many CNN based image deraining methods have been proposed, how to effectively remove raindrops while maintaining background structure remains a challenge that needs to be overcome. Most of the deraining work focuses on removing rain streaks, but in heavy rain images, the dense accumulation of rainwater or the rain curtain effect significantly interferes with the effective removal of rain streaks, and often introduces some artifacts that make the scene more blurry. In this paper, we propose a new network structure R-PReNet for single image deraining with good background structure maintaining. This framework fully utilizes the cyclic recursive structure of PReNet. Moreover, we introduce residual channel prior (RCP) and feature fusion modules for better deraining performance by focusing on background feature information. Compared with the previous methods, our method has significantly improvement effect on the rainstorm image with the artifacts removing and good visual detail restoring.

The (Ti, Nb)(C, B)/IN625 composite coating with both homogeneous and defect-free microstructure were successfully prepared by in situ on the surface of 42CrMo steel using the coupling of the ultra-high-speed laser cladding (EHLA in German) technology with the direct reaction synthesis (DRS) technology, and were comparatively analyzed with the IN625 coating prepared by the EHLA. The microstructure of the fused cladding layer was investigated by selected scanning electron microscopy (SEM), energy dispersive spectrometer (EDS) and double spherical aberration transmission electron microscopy (DSA-TEM). The residual stress distribution on both sides of the fused cladding interface was characterised by nanoindentation stress test based on the modified O&P method and the G&S energy method. The results show that the interface of (Ti, Nb)(C, B)/IN625 composite coatings is affected by about 670 kJ Joule heat released from the in-situ reaction, and the interfacial width reaches 24 μm, so that it is 6 times higher than that of IN625 coating prepared by EHLA, which effectively reduces the stress gradient in the interfacial region and alleviates the stress mismatch on both sides of the interface. However, the surface hardness of (Ti, Nb)(C, B)/IN625 composite coating is lower than that of the IN625 coating, with a value of about 240 HV0.2, and the average wear weight loss was only 10% of that of the IN625 coating, which is on the one hand attributed to the in-situ authigenic TiCB, TiC, NbMo3B4, and NbMo2B2 phases supporting the (Ti, Nb)(C, B)/IN625 composite coating substrate to achieve the abrasion reduction and wear resistance. On the other hand, it is attributed to the formation of nano-equiaxial ultrafine grains in the depth range of 250 nm below the wear surface area by the coupling of the three fields of plastic rheology-heat-force, which dynamically strengthens the wear surface.

Chromium-nickel austenitic stainless steels are widely used due to their high corrosion resistance, good weldability and deformability. To some extent, their application is limited by their mechanical characteristics. As a result of their austenitic structure, increasing the static and dynamic strength of the components can be achieved by surface cold work. Due to the tendency of these steels to undergo intercrystalline corrosion, another approach to improve their mechanical characteristics is the use of low-temperature thermo-chemical diffusion processes. This article proposes a new combined process based on sequentially applied diamond burnishing (DB) and low-temperature gas nitriding (LTGN) to optimally improve the fatigue strength of 304 steel. The essence of the proposed approach is to combine the advantages of the two processes (DB and LTGN) to create a zone of residual compressive stresses in the surface and subsurface layers–the enormous surface residual stresses (axial and hoop) introduced by LTGN, with the significant depth of the compressive zone characteristic of static surface cold working processes. DB (both smoothing and single-pass hardening), in combination with LTGN, achieves a fatigue limit of 600 MPa, an improvement of 36.4% compared to untreated specimens. Individually, smoothing DB, single-pass DB and LTGN achieve 540 MPa, 580 MPa and 580 MPa, respectively. It was found that as the degree of plastic deformation of the surface layer introduced by DB increases, the content of the S-phase in the nitrogen-rich layer formed by LTGN decreases, with resultant increased content of -phase, and a new (also hard) phase: stabilized nitrogen-bearing martensite.

This paper presents geometric analyses of welded frames after free relaxing and vibratory stress relief (VSR). Respected frames were tested components of a prototype packaging machine. Two types of relaxation were carried out to remove stresses introduced as a result of welding process. One of the frame was subjected to free relaxing, while the other one to accelerated vibration relaxing. Detection of the frame geometry changes was performed using a photogrammetric system. In addition, an evaluation of the geometry change was conducted for fifteen variants of steel frame support. A comparative analysis of the geometric deviations of the frames after free and vibratory stress relief confirmed the assumption that the frame after vibration stress relief better reproduces the nominal dimensions. Nevertheless, it should be emphasized that after vibratory stress relief the frame is not subject to further deformation, which is a desirable effect. In the case of free relaxing, the frame undergoes dimensional changes in a random manner. In summary, carrying out accelerated vibratory stress relief allows control of spontaneous dimensional changes in the designed frame of a packaging machine resulting from spontaneous relaxation of stresses arising from the welding process. The shortening of the relaxation process of the welded frame is also an unquestionable advantage.

The effects of 1%~5% cold compression on the mechanical properties, microstructure and residual stress of 7050 aluminum alloy were investigated, and the results were applied to the complex die forging with stiffener structure. The results show that with the increase of cold compression, the tensile strength of forging decreases gradually, but the conductivity and fracture toughness of the forging have little change. TEM microstructure shows that with larger cold compression (5%), the dislocation density was relatively higher, which promoted the precipitation and growth of precipitated phases at positions with higher dislocation density during aging process. The ultrasonic tests of residual stress show that with the increase of cold compression, the residual stress in the forging decreases first, and then increases when cold compression reaches 5%. The results of the die forging of 7050 aluminum alloy with bar structure show that the residual stress of the die forging is obviously eliminated after 2-4% cold compression, and the deformation after machining is obviously less than of the uncompressed forgings. The combination of bar and web is more beneficial to the uniform distribution of residual stress of the die forging with bar structure.

With the rise of piano teaching in recent years, many people have joined the team of piano learners. However, the expensive cost of manual instruction and the unique one-on-one teaching model have made piano learning an extravagant event. Most existing approaches based on the audio modality aim to evaluate piano players' skills. Unfortunately, these methods ignored the information contained in the video, which led to a one-sided and simplistic evaluation of the piano player's skills. More recently, multimodal-based methods are proposed to assess the skill level of piano players using both video and audio information. However, existing multimodal approaches use shallow networks to extract video and audio features, which are deficient in extracting complex spatio-temporal and time-frequency features from piano performance. Furthermore, the fingering and the pitch-rhythm information of the piano performance is contained in the spatio-temporal and time-frequency features, respectively. In the paper, we propose a ResNet-based audio-visual fusion model that combines video and audio features to assess the skill level of piano players. Firstly, ResNet18-3D is used as the backbone network for our visual branches, which can extract feature information from the video data. Then, we consider ResNet18-2D as the backbone network of the aural branch and extract the feature information from the audio data. The extracted video features are fused with the audio features to generate multimodal features for the final piano skill evaluation. The experimental results on the PISA dataset show that our proposed audio-visual fusion model, with a validation accuracy of 70.80%, outperforms the state-of-the-art methods in both performance and efficiency. Then, we also explore the impact of different layers of ResNet on model performance, and the experimental results show that the audio-visual fusion model dealing with the piano skill assessment problem can make full use of both feature information when the number of video features is close to the number of audio features.

Outliers seriously affect the accuracy in geometric model fitting. Previous works in coping with outliers involve threshold selection and scale estimation. However most scale estimators suppose the inlier distribution as a Gaussian model, which usually poorly meets the cases in geometric model fitting. Outliers, considered as the points with big residuals to all the true models, share common items with big values in quantized residual preferences, thus making the outliers gather away from the inliers in quantized residual preference space. In this paper we make use of this outlier consensus in quantized residual preference space by extending the usage of energy minimization to combine the model error and the spatial smoothness in quantized residual preference space for outlier detection. The energy minimization based outlier detection process follows an alternate sampling and labeling framework. After the outlier detection, an ordinary energy minimization method is employed to optimize the inlier labels, which also follows the framework of alternate sampling and labeling. The experimental results in this study show that the energy minimization based outlier detection method can detect most of the outliers in the data. Furthermore, the proposed energy minimization based inlier segmentation process segments the inliers into different models quite accurately. Overall, the performance of the proposed method is better than most of the state-of-the-art methods.

Keywords: Fatigue crack propagation; Welding residual stress; Plastic re-distribution; Stress ratio effect

Fastener holes are among the most common natural stress concentrators in metal structures. The life cycles of various structural elements, such as those in aircraft structures, automobiles, and rail-end bolt joints are limited by fatigue damage around the holes. An effective approach to delay the formation and growth of fatigue macrocracks is to introduce residual hoop compressive stresses around the holes. Two methods have become established in the prestressing of fastener holes in aircraft components: split sleeve and split mandrel, which implement one-sided processes. The common disadvantage of both methods is the complex procedure due to the need for high accuracy of the initial holes. This article presents a new modified split mandrel method providing the same tightness (interference fit) with a wide tolerance of the pre-drilled hole diameters reducing the number of technological cycle steps and production costs. То implement the new method, a functionally connected tool and a device with a hydraulic drive were developed. An extensive experimental study of 2024-T3 AA specimens was carried out to evaluate the effectiveness of the method under a high scattering of the pre-drilled holes. The new method provided a deep zone of residual hoop compressive stresses on both faces of the specimens after cold working and after hole final reaming. The removal of a plastically deformed layer around the hole of suitable thickness during the final reaming decreased the axial gradient of residual hoop stress distribution. Fatigue tests on a tensile pulsating cycle verified the effectiveness of the modified split mandrel method to significantly increase the fatigue life by 6.6 times based on cycle fatigue strength compared to the conventional case of machining the holes. The obtained S-N curves for three groups of samples with initial hole diameters of 8.0, 8.1, and 8.2 mm which were cold worked with the same tightness of 0.32 mm and final reamed, aligned well indicating that the new method can provide constant fatigue strength for a given stress amplitude.

JIS SUJ2 is most widely used in bearing steels. The advantages are good hardenability, excellent fatigue, wear resistance and comprehensive mechanical properties. For wear resistance and fatigue resistance of the steel are getting more and more attention, the residual stress state and its distribution on the surface of the heat affect zone are critical factors affecting the fatigue life and wear resistance of the parts. In this study, using the SUJ2 as the material to talk about the surface residual stress and retained austenite distribution. Doing the quenching and tempering treatment to obtain different microstructures, using the induction method to re-quench the case region. After the heat treatment, measuring the residual stress and retained austenite volume on the surface by X-ray diffraction, observing the microstructure and comparing the hardness. The results show that the microstructure after heat treatment contains unsolidified carbides, tempered martensite and retained austenite. The residual stress on the surface is the tensile state. In the re-hardened area after induction quenching, the residual stress is all compressive, and the values are more than -800 MPa. In conclusion, in the same output power condition can be observed that the microstructure of the specimens before induction hardening has a significant impact on the effective case depth. The surface residual stress has changed from a tensile to a compressive state. In the re-hardened area, the maximum of the residual compressive stress increased with the austenitized temperature increase.

We investigate some information properties of consecutive k-out-of-n:G systems in light of fractional generalized cumulative residual entropy. We firstly derive a formula to compute fractional generalized cumulative residual entropy related to the system's lifetime and explore its preservation properties in terms of established stochastic orders. Additionally, we obtain useful bounds. To aid practical applications, we propose two non parametric estimators for the fractional generalized cumulative residual entropy in these systems. The efficiency and performance of these estimators are illustrated using simulated and real datasets.

The inverse finite element method (iFEM) is a novel method for reconstructing the full-field displacement of structures by discrete measurement strain. In practical engineering applications, the accuracy of iFEM is reduced due to the positional offset of strain sensors during installation, and errors in structural installation. Therefore, coarse and fine two-stage calibration (CFTSC) method is proposed to enhance the accuracy of the reconstruction of structures. Firstly, the coarse calibration is based on a single-objective particle swarm optimization algorithm to optimize the displacement-strain transformation matrix related to the sensor position. Secondly, as selecting different training data can affect the training effect of self-constructed fuzzy networks, this paper proposes to screen the appropriate training data based on residual analysis. Finally, the experiments of the wing integrated antenna structure verify the efficient of the method on the reconstruction accuracy of the structural body displacement field.

Remote sensing imagery is of great significance for policy decisions, especially for disaster assessment and disaster relief. To ensure the privacy and inviolability of personal buildings, the information containing these buildings must be anonymized during the remote sensing mapping process. Traditional processing methods for these targets in remote sensing mapping are mainly based on manual retrieval and image editing tools, which are inefficient. Deep learning provides a new direction for target hiding. Although the image inpainting method based on deep learning is faster than the manual method, the cost of training calculation is a disadvantage. And the element-wise product operation used in the model increases the risk of vanished or exploded gradients. We propose a Residual Attention Target Hiding (RATH) model for remote sensing target hiding based on deep learning. RATH uses residual attention modules to replace gated convolutions, reducing parameters and mitigating gradient issues. The residual attention module preserves gated convolution performance but provides an adjustable kernel size. RATH retains gated convolutions for dynamic feature selection and balances model depth and width. Furthermore, this paper modifies the contextual attention layer by adjusting the fusion process to enlarge the fusion patch size. Finally, we extend the edge-guided function to preserve the original target information and confound viewers. Ablation studies on an open dataset prove RATH’s efficiency for image inpainting and target hiding. RATH achieves state-of-the-art results with lower complexity. And it has the highest similarity for edge-guided target hiding. RATH enables robust, efficient target hiding for privacy protection in remote sensing imagery while balancing performance and complexity. Experiments show RATH's superiority over existing methods in hiding arbitrary-shaped targets.