Scale Features Research Articles

Aerodynamic characteristics of the retro propulsion landing burn of vertically landing launchers

In the frame of the European funded H2020 project RETALT (retro propulsion-assisted landing technologies), the unsteady aerodynamics of vertically descending and landing launchers have been investigated. In this paper, experimental data of the landing burn tested in the Vertical Free-Jet Facility Cologne at DLR in Cologne are presented. The landing burn was simulated with a cold gas jet of pressurized air opposing the wind tunnel free stream. Tests with several jet conditions were compared to results without active jet. Proper orthogonal decomposition of schlieren recordings and spectral analyses of their time histories are performed and are compared to frequencies in pressure measurements. Dominant frequencies were found, which are strongest at Mach 0.8. Especially, a Strouhal number of 0.2 was found to be most dominant. The intensity of the dominant frequencies can be lowered if the engine is active. The normalized root mean square pressure fluctuations are between 0.1 and 0.3 during the landing maneuver. Additionally, the steady flow features scale well with the ambient pressure ratio and the momentum flux ratio. The unsteady flow field dynamics of the subsonic retro propulsion flow field can likely be linked to large-scale turbulent structures in the supersonic jet, triggering large-scale pressure fluctuations and altering the overall flow field.

Mathematical Model of Logistic Regression for Binary Classification. Part 2. Data Preparation, Learning and Testing Processes

This article reviews the theoretical aspects of logistic regression for binary data classification, including data preparation processes, training, testing, and model evaluation metrics. Requirements for input data sets are formulated, methods of coding categorical data are described, methods of scaling input features are defined and substantiated. A scheme for learning logistic regression using the gradient descent method has been developed to minimize the loss function by the appropriate adjustment of the weights of the features of the sample of objects intended for classification. Features of the construction of recurrent methods of classical and stochastic gradient descent are determined. The requirements for the organization of the data sample for the multi-stage learning model in order to avoid overtraining or undertraining of logistic regression are described. The scheme of testing the trained logistic regression is given and the main quality metrics of binary classification are described. The influence of the height of the classification threshold on the efficiency of logistic regression was noted. According to the results of the work, the directions of perspective research of logistic regression are outlined.

MACCoM: A multiple attention and convolutional cross-mixer framework for detailed 2D biomedical image segmentation

The UNet architecture, which is widely used for biomedical image segmentation, has limitations like blurred feature maps and over- or under-segmented regions. To overcome these limitations, we propose a novel network architecture called MACCoM (Multiple Attention and Convolutional Cross-Mixer) – an end-to-end depthwise encoder-decoder fully convolutional network designed for binary and multi-class biomedical image segmentation built upon deeperUNet. We proposed a multi-scope attention module (MSAM) that allows the model to attend to diverse scale features, preserving fine details and high-level semantic information thus useful at the encoder-decoder connection. As the depth increases, our proposed spatial multi-head attention (SMA) is added to facilitate inter-layer communication and information exchange, enabling the network to effectively capture long-range dependencies and global context. MACCoM is also equipped with a convolutional cross-mixer we proposed to enhance the feature extraction capability of the model. By incorporating these modules, we effectively combine semantically similar features and reduce artifacts during the early stages of training. Experimental results on 4 biomedical datasets crafted from 3 datasets of varying modalities consistently demonstrate that MACCoM outperforms or matches state-of-the-art baselines in the segmentation tasks. With Breast Ultrasound Image (BUSI), MACCoM recorded 99.06 % Jaccard, 77.58 % Dice, and 93.92 % Accuracy, while recording 99.50 %, 98.44 %, and 99.29 % respectively for Jaccard, Dice, and Accuracy on the Chest X-ray (CXR) images used. The Jaccard, Dice, and Accuracy for the High-Resolution Fundus (HRF) images are 95.77 %, 74.35 %, and 95.95 % respectively. The findings here highlight MACCoM's effectiveness in improving segmentation performance and its valuable potential in image analysis.

Fine-Grained Few-Shot Image Classification Based on Feature Dual Reconstruction

Fine-grained few-shot image classification is a popular research area in deep learning. The main goal is to identify subcategories within a broader category using a limited number of samples. The challenge stems from the high intra-class variability and low inter-class variability of fine-grained images, which often hamper classification performance. To overcome this, we propose a fine-grained few-shot image classification algorithm based on bidirectional feature reconstruction. This algorithm introduces a Mixed Residual Attention Block (MRA Block), combining channel attention and window-based self-attention to capture local details in images. Additionally, the Dual Reconstruction Feature Fusion (DRFF) module is designed to enhance the model’s adaptability to both inter-class and intra-class variations by integrating features of different scales across layers. Cosine similarity networks are employed for similarity measurement, enabling precise predictions. The experiments demonstrate that the proposed method achieves classification accuracies of 96.99%, 98.53%, and 89.78% on the CUB-200-2011, Stanford Cars, and Stanford Dogs datasets, respectively, confirming the method’s efficacy in fine-grained classification tasks.

Rapid and versatile, micro-patterning and functionalization of complex structures on ultra-thin and flexible polymeric substrates

Microscale patterning on flexible substrates is important in many applications such as in wearable sensors and microfluidics-based diagnostics, therefore low-cost fabrication methods which are scalable and amenable to mass production have attracted the interest of many companies and research groups. Dry film resists (DFRs) are commercially available materials with properties compatible with their implementation on flexible substrates to cover a wide range of applications, which also offer environmental and sustainability benefits due to the low waste generation compared to the liquid resists. However, there are limited detailed reports in the literature regarding the use of DFRs for the fabrication of microfluidic channels or other micropatterns (i.e., posts) on thin and flexible substrates. Herein we present in detail the fabrication of: a) microfluidic channels of width ranging from 50 μm up to 800 μm, and depth ranging from 30 μm up to 270 μm and b) square posts 80 μm × 80 μm in size and 30 μm in height. Particularly, our method enables the fabrication of ultra-deep microchannels (depth > 250 μm), highly ordered post arrays over large area (appr. 60 cm2), as well as complex designs with hierarchical scale features (80 μm posts inside 800 μm microchannels or micro-nanotexturing inside microchannels) on ultra-thin flexible substrates. To demonstrate the versatility of the method, three different DFRs were used on ultra-thin (30 μm), flexible, single-sided copper-clad polyimide substrates. It is also demonstrated that DFRs can be effectively modified using plasma etching to tune the surface wetting properties towards applications such as pumpless capillary action, where such functionality is required.

Cutout effects on the vibration of sandwich auxetic cylindrical shells with an experimental validation

This study investigates the influence of auxetic core layers on the vibrational characteristics of sandwich cylindrical shells with cut-outs. The analysis utilizes the First-order Shear Deformation Theory (FSDT) and analytical techniques. The irregular behavior of auxetic re-entrante honeycomb cells necessitates the exploration of their effects on various systems. Additionally, the proposed method offers advantages over Finite Element Method (FEM). FEM modeling suffers from increased computational time and reduced accuracy with a large number of elements or high aspect ratio elements. These challenges are particularly significant for shells with geometric features of disparate scales, such as large cylinders with tiny cutouts or cutouts with low aspect ratios. However, the present research addresses these challenges by employing a method that integrates five panels with varying dimensions. Consequently, each section can utilize specific Two-Dimensional Generalized Differential Quadrature (2D-GDQ) grid points, tailored to the size of each side, enabling rapid and accurate prediction of displacement and stress parameters, particularly for shorter sides. The proposed method excels at precisely modeling cutouts while preserving their realistic properties. This is achieved by avoiding geometric and material simplifications and by assigning distinct boundary conditions to each region, including critical areas like corners, internal, and external sides. Moreover, Frequency Response Functions (FRFs) obtained from accelerometer and laser signals, alongside vibration-induced sound wave signals captured by electroacoustic microphones, validate the precision of the numerical calculations.

Subgroups of relational job characteristics and their differences in turnover intention and subjective well-being among nurses: a latent profile analysis

ObjectiveRelational job characteristics include perceived social worth and perceived social influence. Good relational job characteristics mean that nurses have high prosocial behavior. The purpose of this study was to explore the potential profile of nurses’ relational job characteristics, influencing factors and their differences in turnover intention and subjective well-being, thus finding the most suitable clinical relationship job characteristics.MethodsA cross-sectional survey was conducted among 1013 clinical nurses using the general demographic data questionnaire, Relational Job Characteristics scale, Turnover Intention Questionnaire and Campbell index of well-being. A latent profile analysis was performed to explore relational job characteristics latent profiles. Multinomial logistic regression analysis was conducted to examine the predictors of profile membership, and a one-way analysis of variance was applied to compare the turnover intention and subjective well-being in each latent profile.ResultsFive latent profiles were identified and labeled ‘High prosocial job characteristics’ profile (20.7%), ‘Moderate prosocial job characteristics’ profile (41.7%), ‘High social worth-low social impact perceived’ profile (6.3%), ‘Low social worth‐high social impact perceived’ profile (18.8%) and ‘Low prosocial job characteristics’ profile (12.5%). Factors affecting the different types of nurse relationship job characteristics include age, marital status, hospital department, nursing years, professional title and hospital position. Among them, chief nurse, nurses with more than 20 years of nursing experience and obstetrics and gynecology nurses were more likely to be ‘high prosocial job characteristics’ profile. The turnover intention of nurses in ‘high prosocial job characteristics’ profile was significantly lower than that of other profiles, and their subjective well-being was significantly higher than that of other profiles.ConclusionImproving nurses’ perception of social worth and social impact on clinical work can improve nurses’ prosocial behavior and subjective well-being, and reduce their turnover intention. Nursing managers or policy makers can formulate targeted intervention measures according to the influencing factors of potential profiles.

A Multi-Dimensional Data-Driven Study on the Emotional Attachment Characteristics of the Renovation of Beijing Traditional Quadrangles

In recent years, the development of China’s megacities has entered the stage of stock renewal. Research and practice concerning old city renewal in cities with a long history, represented by Beijing, has also become a hot spot for researchers and designers in the fields of urban planning, architecture and landscape architecture. As one of the main spatial components of the old city, Beijing’s traditional quadrangles are closely related to the spatial perception and emotional experience of citizens and tourists due to their near-human scale characteristics. However, current research focuses more on the evaluation of the historical value of the quadrangles in the early stage of renovation and the specific design and construction methods in the process of renovation, and few studies pay attention to the characteristics of the built environment that promote the emotional experience of users after renovation is completed. Under these circumstances, the study focuses on the emotional attachment between people and the spatial composition and built environment characteristics of the renovated traditional Beijing quadrangles; the avant garde small-scale quadrangle renovation type, which has a wider coverage, more types of user, and pays more attention to people’s emotional experience in the renovated space, was selected as the research object. Four typical quadrangle cases were selected for in-depth discussion. Based on the theory of emotional attachment from a multidisciplinary perspective, this study constructed a series of scales that can measure the degree and dimension of emotional attachment between people and the built environment, coupled with local observation and interviews, to obtain multi-dimensional data reflecting attachment, and used SPSS to conduct correlation analysis and exploratory factor analysis to quantitatively explore the effects of different built environment characteristics on attachment. The results show that: (1) As for the material characteristics, the organic integration of traditional and modern materials, structures and colors can effectively enhance people’s positive emotional experience and promote the establishment of emotional attachment. The combination of these characteristics and the process of people’s three-dimensional spatial experience can further enhance the degree of attachment. In addition, the consistency of materials and technologies, the organic integration of the old and the new, and the carrying capacity of the renovation method for traditional history and culture are the basis for promoting this kind of emotional attachment, which needs to be further explored and considered. (2) As for the non-material characteristics, the diverse, variable, recognizable, unique, and digital spatial function settings that respond to changes in people’s need and current developments can significantly promote the establishment of emotional attachment between people and the environment. This further emphasizes the importance of positioning the space in the early stage of the renovation and the operation of the space in the later stage. (3) The results further support the validity and rationality of the series of scales constructed in this study in quantitatively measuring the attachment characteristics between people and the built environment. As a result, the study provides a reference for emotion-oriented design means, research logic and quantitative evaluation methods in the practice and research of urban renovation and renewal in the future.

CSPGNet: Cross-scale spatial perception guided network for tiny object detection in remote sensing images

Tiny object detection in remote sensing images has been a popular and challenging task in recent years. Due to the mismatch of feature scales that tiny objects rely on and the interference from complex surroundings in remote sensing images, traditional object detection algorithms still exhibit poor performance in detecting tiny objects. Based on the above observations, this paper proposes a cross-scale spatial perception guided network (CSPGNet) for tiny object detection in remote sensing images. Specifically, we first designed a cross-scale hierarchical perception module (CSHPM) at the topmost level of the Faster R-CNN backbone network to integrate contextual information from various levels and scales, thereby optimizing the representation of tiny objects in feature scales. Furthermore, to address the issue of information loss that occurs when combining low-resolution feature layers with those generated by the aforementioned module during the fusion process, we have developed an adaptive spatial alignment unit (ASAU) that utilizes variability convolution to adaptively align the spatial information of neighboring feature layers. Finally, we present an attention-guided information integration module (AGIIM), which utilizes large kernel attention to guide the feature information, improving tiny objects' global and local information across various feature layers and mitigating the influence of complex environments on the detection task. Extensive experiments were conducted on two publicly available tiny object datasets, namely AI-TOD and VisDrone2019, and the results demonstrate that our approach achieves higher accuracy compared to the majority of state-of-the-art methods.

Feature aggregation network for small object detection

Due to the miniature scale and limited identifiable features, small objects pose a significant challenge in detection. Improving the accuracy of small object detection is a momentous issue of concern among researchers. Feature pyramid network employs a divide-and-conquer strategy for detecting small objects in low-level networks. However, the limited semantic information in these networks results in suboptimal performance in small object detection. To address this issue, we fully utilize information from all feature levels and propose a Feature Aggregation Network (FAN). We investigate information propagation pathways in neural networks, analyze early fusion and late fusion of features, and introduce a dual top-down pathway that utilizes high-level semantic information to consistently reinforce low-level spatial information. We design a Feature-Aware Module that narrows the semantic gap and steers the network toward learning features that favor small object detection. We employ deformable convolution to accurately locate the boundaries of objects with varying shapes and sizes. FAN can function as a plug-and-play component with minimal computational overhead and be trained end-to-end alongside backbone networks. Extensive experiments are conducted on the COCOs, TinyPerson, and VisDrone datasets. The highly competitive results demonstrate that our approach exhibits robust generalization capabilities and can further improve the accuracy of small object detection.

An MILP model based on a processing strategy of complex multisource constraints for the short-term peak shaving operation of large-scale cascaded hydropower plants

Hydropower with flexible regulation plays an important role in short-term peak shaving operations. However, short-term peak shaving operation is a challenging problem due to the large scale, the nonconvex and nonlinear characteristics, and the complex multisource tasks. This study proposes a mixed integer linear programming (MILP) model, termed MILPoPSC, based on a processing strategy for complex multisource constraints, tailored for short-term peak shaving in large-scale cascaded hydropower plants. The MILP model, designed to incorporate multisource tasks by abstracting them into constraints, ensures that task requirements are met. A novel multisource constraint transformation method is introduced to derive constraint expressions related to power flow, facilitating the unification of constrained variables. Additionally, a classification and integration method based on restriction mode and set theory is proposed to improve solving efficiency by integrating constrains of the same type. The proposed method was applied to 7 hydropower cascade plants in the Wujiang River. The results showed that the linearization method and the processing strategy of complex multisource constraints can successfully reduce the complexity of the MILP model without affecting the solution quality. This indicates that MILPoPSC has good practical value for the short-term peak shaving operation of large-scale cascaded hydropower plants in China.

A study on 3D LiDAR-based point cloud object detection using an enhanced PointPillars network

Abstract The PointPillar target detection algorithm is a mainstream 3D lidar point cloud target detection algorithm that has a fast response speed but low detection accuracy. Addressing the problem of the low detection accuracy of the PointPillar target detection network, we propose an improved PointPillar target detection algorithm that integrates an attention mechanism. The algorithm first introduces the attention mechanism and strengthens the feature extraction module based on PointPillar to realize the amplification of the local information in the three scale feature maps and to better extract the more important feature information. Then, our algorithm adds an anchor free type detector head to further optimize the detector head module. The experimental results show that the optimized PointPillar target detection algorithm has achieved good test results in the KITTI data set. Under medium difficulty, the AOS mode mAP reaches 79.76%, the 3D mode mAP reaches 82.03%, and the BEV mode mAP reaches 82.30%. Compared with that of other point cloud target detection algorithms, the detection accuracy of our algorithm is improved by approximately 10%.

Formation, characterization of gelatine from the scales of Labeo rohita and its comparison with bovine bone gelatine

The study isolated gelatin from Labeo rohita scales and characterized it using Fourier transform infrared spectroscopy (FTIR), X-Ray diffraction (XRD), and scanning electron microscopy (SEM). The resulting gelatine and commercial bovine bone gelatin were found to have crystalline structures, with both being porous and spherical. L. rohita scales yielded 5.45 % of gelatin. The moisture content varied between 11.12 % and 9.02 % for the gelatin, while the ash content varied between 2.17 % and 2.36 %. The protein content was 82.78 % in the fish scales gelatin, while the commercial bovine bone gelatin had 94.27 %. The fat content of gelatin isolated from scales of L. rohita was 1.21 %, whereas fat in commercial bovine bone gelatin was 1.19 %. The fiber contents of gelatin isolated from fish scales was 0.44 % while fiber content of commercial bovine bone gelatin was 0.65 %. The study confirms the high-quality gelatin-bearing characteristics of fish scales, suggesting that it can be produced for various purposes and potentially increase the economic value of fish. The isolated gelatin from fish by-products could also be a valuable resource.

Scale Microornamentation of Six Species of Venomous Snakes from China

The scales of six venomous snake species from China were examined for their ultrastructure. Scanning electron microscopy was used to observe the scales, which were peeled off from the middle back of the snakes. The results revealed significant differences in the microdermatoglyphic structure patterns between the two families, Viperidae and Elapidae. Viperidae exhibited a “polygonal” structural unit, while Elapidae displayed arcs, pores, and strips as their basic structure. Additionally, there were notable differences in ultrastructure within each family. In Viperidae, Ovophis makazayazaya had a flat structural unit, Trimeresurus stejnegeri had a mound-like uplift near the center with a flat surrounding, and Protobothrops mucrosquamatus had an overall uplifted structure. In Elapidae, Ophiophagus hannah showed obvious differences in the size and arrangement of arc structures. The intraarc structure of Bungarus multicinctus resembled vertebral bodies, while that of Sinomicrurus peinani was columnar in shape. The ultrastructural characteristics of snake scales generally exhibited essential differences among advanced taxonomic units. However, there were detailed differences among species within the same family or genus. The initial formation of microornamentation features in scales may be related to the snakes’ habitat environment. Species formation through isolation is accompanied by the differentiation of these characteristics, and once species formation occurs, the features remain stable until the next species differentiation. There were instances of similar structures found in species with distant evolutionary relationships. This suggests that the microdermatoglyphic structure has taxonomic significance but may not necessarily have phylogenetic significance.

Trauma-related symptoms in adolescents: the differential roles of sexual abuse and mentalizing.

Major gaps remain in our knowledge regarding childhood sexual abuse (CSA) related symptoms in adolescent psychiatric inpatients, as well as potential resilience factors like mentalizing. CSA is a risk factor for the early emergence of borderline personality features, posttraumatic stress, and sexual concerns. Mentalizing, which involves the capacity to understand our reactions and that of others in psychological terms, is a resilience factor for self and interpersonal functioning. The aim of this study was to address knowledge gaps by examining the contributions of CSA and mentalizing in a latent factor composed of borderline personality features, posttraumatic stress, and sexual concerns in a sample of adolescent psychiatric inpatients. We hypothesized that CSA and mentalizing would independently explain the variance in this latent factor. Participants were 273 adolescents aged 12-17 recruited from an adolescent inpatient psychiatric clinic. They completed the Reflective Function Questionnaire for Youth (RFQ-Y), the Trauma Symptom Checklist for Children (TSCC), and the Borderline Personality Features Scale for Children (BPFS-C). CSA was assessed using the Child Attachment Interview (CAI), the Computerized Diagnostic Interview Schedule for Children (C-DISC), as well as the Childhood Trauma Questionnaire (CTQ). 27.5% of adolescent psychiatric inpatients reported CSA. CSA and mentalizing were independently associated with a latent factor consisting of posttraumatic stress, borderline personality features, and sexual concerns. CSA explained 5.0% and RF explained 16.7% of the variance of the latent factor. When we consider both the unique and the shared contribution of CSA and mentalizing, the model explained 23.0% of the variance of this factor. CSA and mentalizing independently explained variance in a latent factor constituted of borderline personality features, posttraumatic stress, and sexual concerns. The direct effect of mentalizing was stronger and mentalizing explained comparatively more variance of trauma-related symptoms in adolescent psychiatric inpatients. The findings are consistent with the theory that mentalizing is an internal resilience factor in adolescent psychiatric inpatients. By implication, clinical interventions focused on promoting the development of mentalizing, such as Mentalization Based Treatment, may palliate mental health difficulties manifested by adolescent psychiatric inpatients including those associated with CSA.

Modeling two-dimensional ice shape based on fractal interpolation

The ice accretion data obtained from ice wind tunnel tests reveal a multiscale structure and rough surface. In the follow-up aerodynamic evaluation of icing airfoils, simplified two-dimensional ice shapes are generally used as substitutes, but this simplification changes the aerodynamic effects of the original ice shape. Therefore, finding a simple and effective two-dimensional ice shape simulation method is urgent. Due to the self-similarity characteristics of ice shape surfaces, fractal interpolation is proposed for ice shape simulation. First, the geometric characteristics of the ice shapes are analyzed to determine interpolation points, and an iterative function system is constructed for interpolation simulation. Considering the influence of various characteristics of ice shapes on aerodynamics, interpolation parameters are limited to simulating more realistic ice shapes. High-order numerical simulation methods were utilized to numerically simulate and analyze the aerodynamic characteristics of icing airfoil while also verifying the feasibility of fractal interpolation for simulating ice shapes. The analysis revealed that this method could effectively simulate ice profiles of various feature scales with minimal ice shape data. These simulated shapes closely resemble real ice formations and maintain the original aerodynamic characteristics of the icing airfoil. This method can be used to improve the computational accuracy of ice accretion codes and provide improvement strategies for complex ice shape prediction; thus, this method has great application prospects in engineering.

A coupled computational fluid dynamics-discrete element method modeling for the dynamic behavior of the polar detector in a crushed ice environment during polar exploration

Investigating the dynamic behavior of polar detectors holds significance for the polar exploration of clean energy production. This paper uses computational fluid dynamics and the discrete element method, complemented by laboratory experiments, to systematically explore the water entry dynamics of a projectile passing through a zone of crushed ice accumulation. The research analyzes the influence of different crushed ice accumulation heights (ha) and water entry conditions on cavity formation, flow field distribution, and dynamic characteristics as the projectile passes through the crushed ice zone. Moreover, the influence of multi-body coupling on the movement of crushed ice and fluid is analyzed. The findings reveal alterations in the water entry behavior of the projectile due to the presence of the crushed ice accumulation zone. A notable two-way coupling mechanism between crushed ice and fluid is identified: crushed ice particles influence liquid level fluctuations and cavity evolution, while fluid flow impacts the movement of crushed ice particles. As the height of crushed ice accumulation increases, this coupling effect intensifies, leading to changes in the flow field distribution near the cavity and the hydrodynamic behavior of the projectile. While the alteration in water entry Froude number (Fr) may not significantly alter the evolution pattern of the liquid level flow field, it notably affects the distribution range and formation scale of the flow field characteristics. Additionally, the water entry Fr influences the load characteristics of the projectile as it passes through the crushed ice zone.

An adjustable acoustic metamaterial cell using a magnetic membrane for tunable resonance

Acoustic metamaterials are growing in popularity for sound applications including noise control. Despite this, there remain significant challenges associated with the fabrication of these materials for the sub-100 Hz regime, because acoustic metamaterials for such frequencies typically require sub-mm scale features to control sound waves. Advances in additive manufacturing technologies have provided practical methods for rapid fabrication of acoustic metamaterials. However, there is a relatively high sensitivity of their resonant characteristics to sub-mm deviations in geometry, pushing the limits of additive manufacturing. One way of overcoming this is via active control of device resonance. Here, an acoustic metamaterial cell with adjustable resonance is demonstrated for the sub-100 Hz regime. A functionally superparamagnetic membrane—devised to facilitate the fabrication process by eliminating magnetic poling requirements—is engineered using stereolithography, and its mechanical and acoustic properties are experimentally measured using laser Doppler vibrometry and electret microphone testing, with a mathematical model developed to predict the cell response. It is demonstrated that an adjustable magnetic acoustic metamaterial can be fabricated at ultra-subwavelength dimensions (≤λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\le \\lambda$$\\end{document}/77.5), exhibiting adjustable resonance from 88.73 to 86.63 Hz. It is anticipated that this research will drive new innovations in adjustable metamaterials, including wider frequency ranges.

Comparative analysis of microstructural, compositional, and grazing incidence characteristics of oxide scale on 316L steel: SLM vs. wrought conditions

The aim of this research is to compare the oxidation behavior and characteristics of oxide scale of 316L steel produced by two methods: selective laser melting (SLM) and conventional casting and forming (wrought). To this end, the initial composition and microstructure of samples produced by those methods were first studied. Thermogravimetric analysis (TGA) and long-term isothermal oxidation tests were carried out on the samples and the oxidation kinetics were compared. The oxidized samples were then examined by scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS) and grazing incidence X-ray diffraction (GIXRD). The results indicated that in the temperature range of 600 °C–900 °C, the oxidation resistance of the SLM alloy is lower than that of the wrought alloy, especially at 800 °C. This is attributed to the combined effect of: i) smaller grain size due to the rapid solidification in the SLM alloy that increases the paths of oxygen penetration, ii) lower presence of chromium and manganese elements in the oxide layer and iii) preferential growth of iron oxide in the form of hillocks on the surface. Surface and cross-section analysis of the oxide layers show that iron oxide is dominant on the surface of the SLM sample at temperatures of 600 °C and 800 °C, and at 800 °C its extended hilly growth leads to significant spallation of the oxide scale and an exponential increase in the oxidation rate. However, at 900 °C, with the formation of a continuous oxide layer containing Fe2MnO4 and CrMnO4, the oxidation rate significantly decreases in both alloys.

Illumination-Aware Low-Light Image Enhancement with Transformer and Auto-Knee Curve

Images captured under low-light conditions suffer from several combined degradation factors, including low brightness, low contrast, noise, and color bias. Many learning-based techniques attempt to learn the low-to-clear mapping between low-light and normal-light images. However, they often fall short when applied to low-light images taken in wide-contrast scenes because uneven illumination brings illumination-varying noise and the enhanced images are easily over-saturated in highlight areas. In this article, we present a novel two-stage method to tackle the problem of uneven illumination distribution in low-light images. Under the assumption that noise varies with illumination, we design an illumination-aware transformer network for the first stage of image restoration. In this stage, we introduce the Illumination-aware Attention Block featured with Illumination-aware Multi-head Self-attention, which incorporates different scales of illumination features to guide the attention module, thereby enhancing the denoising and reconstruction capabilities of the restoration network. In the second stage, we innovatively introduce a cubic auto-knee curve transfer with a global parameter predictor to alleviate the over-exposure caused by uneven illumination. We also adopt a white balance correction module to address color bias issues at this stage. Extensive experiments on various benchmarks demonstrate the advantages of our method over state-of-the-art methods qualitatively and quantitatively.