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This work was conducted mainly to provide a healthy and safe monitoring system for the elderly living in the home environment. In this paper, two different target fall detection schemes are proposed based on whether the target is visible or not. When the target is visible, a vision-based fall detection algorithm is proposed, where an image of the target captured by a camera is transmitted to the improved You Only Look Once version 5s (YOLOv5s) model for posture detection. In contrast, when the target is invisible, a WiFi-based fall detection algorithm is proposed, where channel state information (CSI) signals are used to estimate the target's posture with an improved long short-term memory (LSTM) model. In the improved YOLOv5s model, adaptive picture scaling technology named Letterbox is used to maintain consistency in the aspect ratio of images in the dataset, and the weighted bidirectional feature pyramid (BiFPN) and the attention mechanisms of squeeze-and-excitation (SE) and coordinate attention (CA) modules are added to the Backbone network and Neck network, respectively. In the improved LSTM model, the Hampel filter is used to eliminate the noise from CSI signals and the convolutional neural network (CNN) model is combined with the LSTM to process the image made from CSI signals, and thus the object of the improved LSTM model at a point in time is the analysis of the amplitude of 90 CSI signals. The final monitoring result of the health status of the target is the result of combining the fall detection of the improved YOLOv5s and LSTM models with the physiological information of the target. Experimental results show the following: (1) the detection precision, recall rate, and average precision of the improved YOLOv5s model are increased by 7.2%, 9%, and 7.6%, respectively, compared with the original model, and there is almost no missed detection of the target; (2) the detection accuracy of the improved LSTM model is improved by 15.61%, 29.36%, and 52.39% compared with the original LSTM, CNN, and neural network (NN) models, respectively, while the convergence speed is improved by 90% compared with the original LSTM model; and (3) the proposed algorithm can meet the requirements of accurate, real-time, and stable applications of health monitoring. [ABSTRACT FROM AUTHOR]

The flexoelectric effect, garnering extensive attention in recent years, is an electro-mechanical coupled gradient effect that widely exists in dielectric materials and holds great potential for applications in structural sensing and actuation. The parabolic shell structure, characterized by line focusing, finds widespread use in key structural components such as solar trough collectors and communication antennas. Distributed sensing of the structural states of these parabolic shells is critical for vibration control, health monitoring, and shape control of precision structural systems. Therefore, flexoelectric sensing research based on parabolic shell structure has become an important topic. This study establishes a mathematical model for flexoelectric sensing in a parabolic shell with four-sided simply supported boundary conditions. The model is based on the direct flexoelectric effect, and thin shell assumption, and incorporates specific Lamé parameters and curvature radius. The electro-mechanical strain gradient/signal generation characteristics and distributed modal flexoelectric signals on the parabolic shell are investigated. The sensing signal under the open-circuit conditions is deduced, and the flexoelectric sensing signal and sensing characteristics of different modes are analyzed. The formulation of the flexoelectric neural sensing signal for the parabolic shell structure is provided and divided into two components: a circumferential bending component and a longitudinal bending component. In the case studies, the effects of design parameters such as flexoelectric sensor thickness, size, and aspect ratios are evaluated and compared. The analysis and results of this study offer a theoretical foundation and reference for refining the design parameters of the flexoelectric sensor and determining its optimal sensing position, and potentially paving the way for new applications of flexoelectric sensing technology. [ABSTRACT FROM AUTHOR]

Robust monocular depth estimation (MDE) aims at learning a unified model that works across diverse real-world scenes, which is an important and active topic in computer vision. In this paper, we present Megatron_RVC, our winning solution for the monocular depth challenge in the Robust Vision Challenge (RVC) 2022, where we tackle the challenging problem from three perspectives: network architecture, training strategy and dataset. In particular, we made three contributions towards robust MDE: (1) we built a neural network with high capacity to enable flexible and accurate monocular depth predictions, which contains dedicated components to provide content-aware embeddings and to improve the richness of the details; (2) we proposed a novel mixing training strategy to handle real-world images with different aspect ratios, resolutions and apply tailored loss functions based on the properties of their depth maps; (3) to train a unified network model that covers diverse real-world scenes, we used over 1 million images from different datasets. As of 3rd October 2022, our unified model ranked consistently first across three benchmarks (KITTI, MPI Sintel, and VIPER) among all participants. [ABSTRACT FROM AUTHOR]

With the increasing application of ceramic structural parts, how to realize the ductile cutting of ceramics and high-quality machining of ceramic structural parts have become an important research direction. In this paper, the micromilling of fully sintered zirconia ceramics is studied using customized PCD end mills with three different rake angles. The critical thickness for microcutting of ceramics during the micromilling process is theoretically analyzed and experimentally identified. Then, the ductile cutting of ceramic materials is to be realized by controlling the key cutting parameter (feed per tooth f z) considering the critical cutting thickness. Under the condition of ductile cutting, the high-aspect-ratio vortex curved thin walls of fully sintered zirconia ceramics are successfully fabricated. Furthermore, the tool wear of the customized PCD end mill is analyzed. The tool geometric parameters that are beneficial to ensure both ductile cutting and high tool life are identified. [ABSTRACT FROM AUTHOR]

Image retargeting (IR) aims to fit different display terminals by changing the image aspect ratio while retaining its important content. The IR methods utilize a saliency map to predict the importance of pixels; however, existing methods assume that multiple objects within the saliency map have the same importance, which does not mimic the human attention mechanism and thus results in inadequate protection of the main content during the retargeting operation. To solve these problems, we propose an image retargeting network via relative saliency (IRNet-RS), which has three key steps: relative saliency detection, edge detection, and IR operation. By using a relative saliency stratified supervision module and saliency rank guidance for the refinement module, IRNet-RS detects the salient objects in an image, allocates different saliency weights to them to obtain a relative saliency map, and then fuses it with the edge detection map to generate an importance map. An adaptive duplicate convolution module is also designed to help the network learn the pixel-by-pixel shift map from the source to the target grid, followed by retargeting the image using pixel shifting. Our experimental results show that the proposed IRNet-RS achieves better performance than alternative methods in terms of saliency rank prediction and retargeting results. [ABSTRACT FROM AUTHOR]

The simulator is an indispensable device for the study of high-altitude electromagnetic pulse (HEMP) effects. In order to improve the quality of the electric field waveforms of the HEMP simulator, we carried out simulation and experimental studies for the biconical-wire grid antenna. A new bicone-wire grid antenna HEMP simulator was designed by using skeletonized cones instead of solid cones, which can substantially improve the mobility of the simulator. The effects of different aspect ratios and different cone structures of the simulator on the waveform and distribution characteristics of the electric field were analyzed. The accuracy of the simulation results was verified by testing the bicone-wire grid antenna simulator with the new structure. The results obtained are as follows: First, the simulator can provide a HEMP environment compliant with the International Electrotechnical Commission (IEC) standard in an area of 5 × 6 × 2 m3. Second, the simulator possesses optimal waveform quality and working space among simulators with different aspect ratios. Finally, the influence of using skeletonized cones instead of solid cones on the simulator's electric field waveforms is negligible, and the mobility of the new simulator is significantly improved compared to the original simulator. [ABSTRACT FROM AUTHOR]

Natural fiber-reinforced plastic (NFRP) composites are ecofriendly and biodegradable materials that offer tremendous ecological advantages while preserving unique structures and properties. Studies on using these natural fibers as alternatives to conventional synthetic fibers in fiber-reinforced materials have opened up possibilities for industrial applications, especially for sustainable manufacturing. However, critical issues reside in the machinability of such materials because of their multiscale structure and the randomness of the reinforcing elements distributed within the matrix basis. This paper reports a comprehensive investigation of the effect of microstructure heterogeneity on the resultant behaviors of cutting forces for NFRP machining. A convolutional neural network (CNN) links the microstructural reinforcing fibers and their impacts on changing the cutting forces (with an estimated R-squared value over 90%). Next, a model-agnostic explainable machine learning approach is implemented to decipher this CNN black-box model by discovering the underlying mechanisms of relating the reinforcing elements/fibers' microstructures. The presented xml approach extracts physical descriptors from the in-process monitoring microscopic images and finds the causality of the fibrous structures' heterogeneity to the resultant machining forces. The results suggest that, for the heterogeneous fibers, the tightly and evenly bounded fiber elements (i.e., with lower aspect ratio, lower eccentricity, and higher compactness) strengthen the material and thereafter play a significant role in increasing the cutting forces during NFRP machining. Therefore, the presented framework of the explainable machine learning approach opens an opportunity to discover the causality of material microstructures on the resultant process dynamics and accurately predict the cutting behaviors during material removal processes. [ABSTRACT FROM AUTHOR]

As an important part of the power system, it is necessary to ensure the safe and stable operation of transmission lines. Due to long-term exposure to the outdoors, the lines face many insecurity factors, and foreign object intrusion is one of them. Traditional foreign object (bird's nest, kite, balloon, trash bag) detection algorithms suffer from low efficiency, poor accuracy, and small coverage, etc. To address the above problems, this paper introduces the RCDAM-Net. In order to prevent feature loss or useful feature compression, the RevCol (Reversible Column Networks) is used as the backbone network to ensure that the total information remains unchanged during feature decoupling. DySnakeConv (Dynamic Snake Convolution) is adopted and embedded into the C2f structure, which is named C2D and integrates low-level features and high-level features. Compared to the original BottleNeck structure of C2f, the DySnakeConv enhances the feature extraction ability for elongated and weak targets. In addition, MPDIoU (Maximum Performance Diagonal Intersection over Union) is used to improve the regression performance of model bounding boxes, solving the problem of predicted bounding boxes having the same aspect ratio as true bounding boxes, but with different values. Further, we adopt Decoupled Head for detection and add additional auxiliary training heads to improve the detection accuracy of the model. The experimental results show that the model achieves mAP50, Precision, and Recall of 97.98%, 98.15%, and 95.16% on the transmission tower line foreign object dataset, which is better to existing multi-target detection algorithms. [ABSTRACT FROM AUTHOR]

Viscoelastic focusing has emerged as a promising method for label-free and passive manipulation of micro and nanoscale bioparticles. However, the design of microfluidic devices for viscoelastic particle focusing requires a thorough comprehensive understanding of the flow condition and operational parameters that lead to the desired behavior of microparticles. While recent advancements have been made, viscoelastic focusing is not fully understood, particularly in straight microchannels with rectangular cross sections. In this work, we delve into inertial, elastic, and viscoelastic focusing of biological cells in rectangular cross-section microchannels. By systematically varying degrees of fluid elasticity and inertia, we investigate the underlying mechanisms behind cell focusing. Our approach involves injecting cells into devices with a fixed, non-unity aspect ratio and capturing their images from two orientations, enabling the extrapolation of cross-sectional equilibrium positions from two dimensional (2D) projections. We characterized the changes in hydrodynamic focusing behaviors of cells based on factors, such as cell size, flow rate, and fluid characteristics. These findings provide insights into the flow characteristics driving changes in equilibrium positions. Furthermore, they indicate that viscoelastic focusing can enhance the detection accuracy in flow cytometry and the sorting resolution for size-based particle sorting applications. By contributing to the advancement of understanding viscoelastic focusing in rectangular microchannels, this work provides valuable insight and design guidelines for the development of devices that harness viscoelastic focusing. The knowledge gained from this study can aid in the advancement of viscoelastic particle manipulation technique and their application in various fields. [ABSTRACT FROM AUTHOR]

To address the shortcomings of the object detection model in the training process, such as the allocation of positive and negative samples without considering the aspect ratio of the ground-truth box and the poor adaptability to different distributions of objects, this paper proposed the ratio-prior and loss-aware assignment（RLA） algorithm. RLA didn’t change the structure of the original detection model, firstly it selected an equal proportion of the central sampling area based on the aspect ratio of the ground-truth box, then calculated the integrated loss of the anchor points, considered the actual distribution of objects within the ground-truth box, and finally distinguished between positive and negative samples by means of a dynamic loss threshold. The algorithm solved the problems of poor adaptability and difficulty in selecting the best positive samples based on IoU allocation, and the sample allocation for eccentric objects and objects with different aspect ratios was more reasonable. Compared with existing sample allocation algorithms, the algorithm outperforms the MS COCO dataset by 1. 66% over the AP of the baseline FCOS, and 0. 76% and 0. 24% over the AP of the ATSS algorithm and the PAA algorithm respectively when the model structure is the same, demonstrating the effectiveness of the RLA algorithm. [ABSTRACT FROM AUTHOR]

The target detection algorithm for common objects has achieved good results, but the detection accuracy and speed of the target detection algorithm for Unmanned Aerial Vehicle (UAV) need to be improved. Unmanned Aerial Vehicle (UAV) images are characterized by small targets, difficult to identify in the background image, clustering and sparse distribution of targets, etc. Many researchers have proposed the clustering target detection method (ClusDet) for Unmanned Aerial Vehicle (UAV) images. However, due to the large differences in target scales and uneven distribution of targets in UAV images, showing long-tailed distribution, the traditional ClusDet algorithm tends to truncate large and medium targets in the process of clustering; in the detection process, the fixed-threshold NMS method in the ClusDet algorithm is difficult to adaptively detect targets of different sizes, clustering and mutual occlusion. To address the above problems, this paper proposes an adaptive clustered target detection algorithm based on Unmanned Aerial Vehicle (UAV) images under long-tail distribution. The method is divided into three sub-networks: the adaptive clustering sub-network, which outputs several segmented images of small target clustering regions by extracting potential small target clustering regions in Unmanned Aerial Vehicle (UAV) aerial images; the segmentation and filling sub-network, which fills the images with disproportionate aspect ratio for the output of the adaptive clustering network to keep the size of the images within the reasonable range required by the detection network; and the detection sub-network, which detects the targets within the reasonable range required by the detection network by introducing attention mechanism, using variable threshold NMS, and training using sample balancing strategy effectively improve the detection accuracy of targets in the clustered region. Trained in VisDrone 2019 dataset, the simulation results show that the Unmanned Aerial Vehicle (UAV) image adaptive clustering target detection method based on long-tailed distribution has a large improvement in the detection accuracy of small targets, and can effectively improve the detection accuracy of the model for targets in the aggregation region, while the model has good generalization ability. [ABSTRACT FROM AUTHOR]

A Machine and Deep Learning (MLDL) methodology is developed and applied to give a high fidelity, fast surrogate for 2D resistive MagnetoHydroDynamic (MHD) simulations of Magnetic Liner Inertial Fusion (MagLIF) implosions. The resistive MHD code GORGON is used to generate an ensemble of implosions with different liner aspect ratios, initial gas preheat temperatures (that is, different adiabats), and different liner perturbations. The liner density and magnetic field as functions of x, y, and z were generated. The Mallat Scattering Transformation (MST) is taken of the logarithm of both fields and a Principal Components Analysis (PCA) is done on the logarithm of the MST of both fields. The fields are projected onto the PCA vectors and a small number of these PCA vector components are kept. Singular Value Decompositions of the cross correlation of the input parameters to the output logarithm of the MST of the fields, and of the cross correlation of the SVD vector components to the PCA vector components are done. This allows the identification of the PCA vectors vis-a-vis the input parameters. Finally, a Multi Layer Perceptron (MLP) neural network with ReLU activation and a simple three layer encoder/decoder architecture is trained on this dataset to predict the PCA vector components of the fields as a function of time. Details of the implosion, stagnation, and the disassembly are well captured. Examination of the PCA vectors and a permutation importance analysis of the MLP show definitive evidence of an inverse turbulent cascade into a dipole emergent behavior. The orientation of the dipole is set by the initial liner perturbation. The analysis is repeated with a version of the MST which includes phase, called Wavelet Phase Harmonics (WPH). While WPH do not give the physical insight of the MST, they can and are inverted to give field configurations as a function of time, including field-to-field correlations. [ABSTRACT FROM AUTHOR]

A stepped copper microcolumn array was fabricated based on the UV-LIGA technology and microelectroforming process using THB-151N photoresist. In order to solve the problems of difficult development of 20 μm blind microvia and "resist retention" at the bottom during THB-151N photoresist development, a submerged bidirectional megasonic assisted development method was proposed. Comsol Multiphysics software was used to analyze the mass transfer process of developer in blind microvia under different megasonic power densities and aspect ratios, and the mass transfer coefficient was used to characterize the process. According to the actual working conditions, the optimized megasonic power density of 3.2 W/cm2 and aspect ratio of 1.5 are selected and the megasonic assisted development was studied experimentally. Besides, aiming at the poor verticality of side wall of the blind microvia due to inappropriate exposure dose of THB-151N photoresist film, the effect of exposure dose on the verticality of side wall of the blind microvia was discussed by lithography experiment and the Angle β is introduced to measure the verticality of the side wall. The empirical equation between exposure dose and thickness of the film was fitted.On the basis of the above technological methods and experimental study, stepped Cu microcolumn arrays of 4 × 6 with a height of 300 μm, overall aspect ratio of 15:1 and minimum side length of 20 μm were fabricated. [ABSTRACT FROM AUTHOR]

This paper proposes the design of a 360° map establishment and real-time simultaneous localization and mapping (SLAM) algorithm based on equirectangular projection. All equirectangular projection images with an aspect ratio of 2:1 are supported for input image types of the proposed system, allowing an unlimited number and arrangement of cameras. Firstly, the proposed system uses dual back-to-back fisheye cameras to capture 360° images, followed by the adoption of the perspective transformation with any yaw degree given to shrink the feature extraction area in order to reduce the computational time, as well as retain the 360° field of view. Secondly, the oriented fast and rotated brief (ORB) feature points extracted from perspective images with a GPU acceleration are used for tracking, mapping, and camera pose estimation in the system. The 360° binary map supports the functions of saving, loading, and online updating to enhance the flexibility, convenience, and stability of the 360° system. The proposed system is also implemented on an nVidia Jetson TX2 embedded platform with 1% accumulated RMS error of 250 m. The average performance of the proposed system achieves 20 frames per second (FPS) in the case with a single-fisheye camera of resolution 1024 × 768, and the system performs panoramic stitching and blending under 1416 × 708 resolution from a dual-fisheye camera at the same time. [ABSTRACT FROM AUTHOR]

The results of an experimental study on the buckling of a vertically cantilevered plate under corner twisting forces are reported. In this configuration, an interesting and somewhat counter-intuitive behavior is observed in which a laterally loaded slender panel exhibits a subtle instability characterized by nonlinear out-of-plane corner deflections. This is one of the few studies in the literature that investigates the buckling of cantilevered plates and appears to be the only experimental study of buckling under twisting loads, i.e., loading the plate with point loads at the free corners and in opposite directions. This paper discusses the practical aspects of experimental verification and methodology and evaluates the effect of the plate aspect ratio (AR) on the nondimensional buckling load in this configuration. In addition, experimental results are compared to finite element analysis (FEA) simulations performed on accessible software. There is generally good agreement between the experiment and the finite element simulation for the change of buckling load with plate aspect ratio. The nondimensional buckling load appears to decrease asymptotically with increasing aspect ratio, though the correlation between experiment and computation is less consistent as aspect ratio increases due to experimental and computational limitations. [ABSTRACT FROM AUTHOR]

On nanopillar-arrayed superhydrophobic surfaces, the contact time of oblique nanodroplet impacts is for the first time investigated via molecular dynamics simulations. Here, oblique nanodroplet impacts are triggered by nanodroplets impacting superhydrophobic surfaces under various impact angles, α. The simulation results show that owing to the non-axisymmetry of spreading factors on nanopillar-arrayed superhydrophobic surfaces, the contact time of oblique nanodroplet impacts is always less on nanopillar-arrayed rather than smooth superhydrophobic surfaces under same impact angles. As the impact angle is increased from 5° to 65°, that is, under different impact conditions, the non-axisymmetry is more remarkable as α > 35° instead of α < 35° at the low, medium, and high normal Weber numbers, Wen. Hence, the contact time is sharp as α > 35° and then slowly reduced as α < 35° at the low, medium, and high Wen, at which the drastically increased sliding length as α > 35° further promotes the rapid reduction in contact time. As the impact angle is constant at 35°, the non-axisymmetry is more remarkable as h/w < 1 instead of h/w > 1 as the aspect ratio of nanopillars, h/w, is increased, that is, under different surface conditions. Hence, the contact time is sharp as h/w < 1 and then slowly reduced as h/w > 1 at the low, medium, and high Wen, at which the drastically reduced sliding length as h/w > 1 further hinders the rapid reduction in contact time. [ABSTRACT FROM AUTHOR]

Blasting demolition plays an important role in building demolition, and numerical-simulation methods can facilitate the optimization of blasting-demolition schemes. To study the influence of the delay-time differences between columns on the blasting and demolition effect of small-aspect-ratio reinforced concrete structures, a separated common node model of small-aspect-ratio structures was established through the finite element numerical simulation software ANSYS/LS-DYNA. The results showed that the delay-time differences between columns influenced the position of the plastic hinge of the structure, and the plastic hinge was located at the end of the back span beam when the delay-time difference was 0.1~0.3 s. The plastic hinge moves to the top of the back row of the columns of the first floor at 0.4~0.8 s and to the end of each span of the beams at 0.9 s. When the plastic hinge is at the end of the beam, the orientation axis is located at the bottom of the back row of the columns of the first floor. Moreover, when the plastic hinge is at the top of the back row of the columns of the first floor, the orientation axis is at the same position as the plastic hinge. The delay-time difference also affects the recoil distance of the structure as well as the height of the burst pile. To ensure that the structure can be successfully collapsed under the preconditions of disintegration, for a small height-to-width ratio structure, the appropriate intercolumn delay-time difference is 0.4~0.5 s. [ABSTRACT FROM AUTHOR]

Background: Reflectance confocal microscopy (RCM) allows for real‐time in vivo visualization of the epidermis at the cellular level noninvasively. Parameters relating to tissue architecture can be extracted from RCM images, however, analysis of such images requires manual identification of cells to derive these parameters, which can be time‐consuming and subject to human error, highlighting the need for an automated cell identification method. Methods: First, the region‐of‐interest (ROI) containing cells needs to be identified, followed by the identification of individual cells within the ROI. To perform this task, we use successive applications of Sato and Gabor filters. The final step is post‐processing improvement of cell detection and removal of size outliers. The proposed algorithm is evaluated on manually annotated real data. It is then applied to 5345 images to study the evolution of epidermal architecture in children and adults. The images were acquired on the volar forearm of healthy children (3 months to 10 years) and women (25–80 years), and on the volar forearm and cheek of women (40–80 years). Following the identification of cell locations, parameters such as cell area, cell perimeter, and cell density are calculated, as well as the probability distribution of the number of nearest neighbors per cell. The thicknesses of the Stratum Corneum and supra‐papillary epidermis are also calculated using a hybrid deep‐learning method. Results: Epidermal keratinocytes are significantly larger (area and perimeter) in the granular layer than in the spinous layer and they get progressively larger with a child's age. Skin continues to mature dynamically during adulthood, as keratinocyte size continues to increase with age on both the cheeks and volar forearm, but the topology and cell aspect ratio remain unchanged across different epidermal layers, body sites, and age. Stratum Corneum and supra‐papillary epidermis thicknesses increase with age, at a faster rate in children than in adults. Conclusions: The proposed methodology can be applied to large datasets to automate image analysis and the calculation of parameters relevant to skin physiology. These data validate the dynamic nature of skin maturation during childhood and skin aging in adulthood. [ABSTRACT FROM AUTHOR]

We study how a bound notebook page can be folded once so that the page is visible when the notebook is closed and becomes a bookmark. An additional practical requirement is that the folded page stays within the same side of the bind, so that it does not get creased further when one closes the notebook. This simple problem displays a remarkably rich bifurcation behavior, which can be analyzed using undergraduate calculus. The main finding is that the optimal way of folding the page depends on the page's aspect ratio and changes abruptly when the latter exceeds the critical value of 1.207 11. This behavior also resembles a first-order phase transition in thermodynamics despite its geometric origin. [ABSTRACT FROM AUTHOR]

Physical experiments provide the best representation of foundation behaviour in any environmental conditions offshore; however, they are very costly, especially since the on-site conditions vary. Hence, it is not a cost-effective approach. In contrast, numerical modelling is relatively cheaper, but requires long computation time and a high level of expertise to perform a successful numerical calculation. The main objective of this paper is to develop a simple and fast response analysis that requires less computational time and expertise. First, an analytical model is developed to study the dynamic behaviour of the pile by extending the beam on an elastic foundation. Second, the analytical results are verified by using the finite-element software, Abaqus. Finally, a parametric study is carried out with the established numerical calculation model to investigate the modal shapes of the pile model, limit states of the offshore wind turbine (OWT) and pile capacity for different loading conditions and aspect ratios. The proposed analytical model demonstrates a degree of error less than 10%, indicating great reliability and the potential for practical application. The parametric results indicate that the OWT is more likely to fail by yielding than buckling and the soil resistance at the pile periphery is highly affected by the aspect ratio. [ABSTRACT FROM AUTHOR]

Static mixer studies in literature have often been reported for idealized systems with a concentric injected flow. These do not account for real-world mixing scenarios where the additive comes in through an injector. Depending on the injector geometry, the injected flow, and pipe Reynolds number, the injected flow may end up off-centered. This impacts the static mixer performance as the first few elements are unable to provide efficient mixing. This paper discusses a parametric study of twisted-tape mixers to numerically ascertain their performance for Newtonian flows. It focuses on industrially relevant mixers which operate with some inexactness, e.g., off-center flow of the additive. Effect on mixing of angle of twist and aspect ratio of the individual element along with Reynolds number are studied; and the Reynolds number dependency of mixer geometry for optimal performance is established. This is done without using some oft-used approximations/assumptions in previous literature like zero-thickness mixer walls, 2d analysis, periodic boundary conditions etc. Two non-dimensional quantities, i.e., Coefficient of Variation (CoV) and Z -factor, are used to assess and quantify the operation of the mixers. For idealized systems, increasing the number of elements by reducing the aspect ratio and/or the twist angle is seen to improve mixing for all Reynolds numbers tested (1−100). However, for a real-world mixer constrained by its total length/pressure drop, mixing is proven to be an optimization problem between increasing the number of flow-splits by introducing a greater number of elements and mitigating the anomalous flow (uneven split) caused due to it. For Re ≤ 10, reduction of twist angle from 180∘ to 140∘ improves mixing for only aspect ratios greater than approximately 0.9. For Reynolds number of 100, the 140∘ twist angle outperforms the 180∘ twist angle for all aspect ratios studied. In summary, there is no combination of twist angle and aspect ratio that is a universally optimal solution and these parameters must be decided subjectively based on multiple other input parameters for a given system. • Optimizes the mixer geometry for non-ideal conditions found in manufacturing units. • Establishes a parameter space for twisted tape mixers in terms of Reynolds number, aspect ratio, and twist angle. • Input variables are studied in conjunction with each other instead of in isolation. • Demonstrates the necessity of system-level study, analysis, and optimization of twisted tape mixers. • Concludes that there is no universally applicable or "silver-bullet" geometry that is optimal for all input conditions. [ABSTRACT FROM AUTHOR]

The in-plane seismic behavior of unreinforced masonry (URM) structures is closely related to the aspect ratio of the wall and vertical load. The purpose of this study was to investigate the difference between the failure mode of the model and the horizontal load using the finite element model (FEM) under the action of aspect ratio (0.50 to 2.00) and vertical load (0.2 MPa to 0.70 MPa). The overall macro model was established using the Abaqus software, and the corresponding simulation was performed. The simulation results indicate that: i) the shear failure and flexural failure were the main failure modes of masonry walls; ii) shear failure could be viewed as the main failure mode of the model when the aspect ratio was less than 1.00; however, the flexural failure was considered to be the main failure mode of the model once the aspect ratio was greater than 1.00; iii) when a vertical load of 0.20 MPa was applied to the model, only flexural failure was observed, regardless of whether the aspect ratio of the model increased or decreased; the flexural shear mixed failure was captured within the range of 0.30 MPa– 0.50 MPa; the shear failure was the main failure mode within the range of 0.60 MPa– 0.70 MPa; and iv) the wall with an aspect ratio less than 1.00 could bear a higher horizontal load, and the increase in vertical load can significantly improve the horizontal load of the wall. In contrast, once the aspect ratio of the wall reaches or exceeds 1.00, the increase in the vertical load has little effect on the increase in the horizontal load of the wall. [ABSTRACT FROM AUTHOR]

High aspect ratio (HAR) structures have many promising applications such as biomedical detection, optical spectroscopy, and material characterization. Bottom-up self-assembly is a low-cost method to fabricate HAR structures, but it remains challenging to control the structure dimension, shape, density, and location. In this paper, an optimized top-down method using a combination of pseudo-Bosch etching and wet isotropic thinning/sharpening is presented to fabricate HAR silicon (Si) nanopillar and nanocone arrays. To achieve these structure profiles, electron beam lithography and reactive ion etching were carried out to fabricate silicon pillars having a nearly vertical sidewall, followed by thinning or sharpening by wet etching with a mixture of hydrofluoric (HF) acid and nitric acid (HNO3). For the dry etching step using the pseudo-Bosch process, the sidewall angle is largely dependent on the SF6/C4F8 gas flow ratio, and it was found that a vertical profile can be attained with a ratio of 22/38. For the wet etching process, a very large HNO3/HF volume ratio is shown to give smooth etching with a slow and controllable etching rate. The final structure profile also depends on the pattern density/array periodicity. When the array period is large, silicon nanopillar is thinned down, and its aspect ratio can reach 1:135 with a sub-100 nm apex. When the pillar array becomes very dense (periodicity much smaller than height), a very sharp nanocone structure is obtained after wet etching with an apex diameter under 20 nm. [ABSTRACT FROM AUTHOR]

With the increasing variety of display devices, image retargeting has become an indispensable technology for adjusting the aspect ratio of images to adapt to different display terminals. Since the retargeting operation would cause geometric distortion and content loss of the image, the image retargeting quality assessment (IRQA) is necessary to guide the retargeting algorithm's optimization, selection, and design. Our paper mainly works for systematically reviewing the state-of-the-art technologies in IRQA. And then, this paper further discusses image registration algorithms for matching the original image and the retargeted image. Next, we investigate the feature measurement methods for image retargeting quality evaluation. To facilitate the quantitative assessment of the IRQA methods, this paper gives a list of publicly open datasets and the performance of the mainstream methods. Finally, some promising research directions towards IRQA are pointed out. From this survey, engineers from the industry may find skills to improve their image retargeting systems, and researchers from academia may find ideas to conduct some innovative work. [ABSTRACT FROM AUTHOR]

Simple Summary: The timely and accurate detection of ewe estrus behavior in precision animal husbandry is an important research topic. The timely detection of estrus ewes in mutton sheep breeding can not only protect the welfare of ewes themselves, but also improve the interests of breeding enterprises. With the continuous increase in the scale of mutton sheep breeding and the gradual intensification of breeding methods, the traditional manual detection methods require high labor intensity, and the contact sensor detection methods will cause stress reaction problems in ewes. In recent years, the rapid development of deep learning technology has brought on new possibilities. We propose a method for the recognition ewe estrus based on a multi-target detection layer neural network. The results show that the method can meet the requirements of timely and accurate detection of ewe estrus behavior in large-scale mutton sheep breeding. There are some problems with estrus detection in ewes in large-scale meat sheep farming: mainly, the manual detection method is labor-intensive and the contact sensor detection method causes stress reactions in ewes. To solve the abovementioned problems, we proposed a multi-objective detection layer neural network-based method for ewe estrus crawling behavior recognition. The approach we proposed has four main parts. Firstly, to address the problem of mismatch between our constructed ewe estrus dataset and the YOLO v3 anchor box size, we propose to obtain a new anchor box size by clustering the ewe estrus dataset using the K-means++ algorithm. Secondly, to address the problem of low model recognition precision caused by small imaging of distant ewes in the dataset, we added a 104 × 104 target detection layer, making the total target detection layer reach four layers, strengthening the model's ability to learn shallow information and improving the model's ability to detect small targets. Then, we added residual units to the residual structure of the model, so that the deep feature information of the model is not easily lost and further fused with the shallow feature information to speed up the training of the model. Finally, we maintain the aspect ratio of the images in the data-loading module of the model to reduce the distortion of the image information and increase the precision of the model. The experimental results show that our proposed model has 98.56% recognition precision, while recall was 98.04%, F1 value was 98%, mAP was 99.78%, FPS was 41 f/s, and model size was 276 M, which can meet the accurate and real-time recognition of ewe estrus behavior in large-scale meat sheep farming. [ABSTRACT FROM AUTHOR]

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Background: In the present work, undamped free vibration responses are numerically explored on stiffened rectangular plates following a novel stiffener designing concept where arbitrarily orientated stiffeners are branched out from a central longitudinal stiffener. Purpose: The correlating frequencies with corresponding orthogonally traditional stiffened plates are simultaneously compared. The stiffened plates with arbitrarily oriented branching and their orthogonal counterparts are analyzed numerically by the finite element methodology employing an efficient software package ANSYS. Methods: The plates and stiffeners of all the stiffened plates are modeled as the 2-D geometries and discretized considering quadratic shell elements. The block Lanczos eigenvalue extraction algorithm has been employed through ANSYS to obtain the undamped natural frequencies and corresponding mode shapes. Investigations on parameters controlling stem lengths and the placements and orientations of the branch stiffeners are explored. Simultaneous comparison with orthogonal stiffener arrangements evaluated for identical stiffener depth is conducted. Results: Higher and lower magnitudes of correlating characteristic frequencies, such as the fundamental frequencies, are obtained considering simple alterations in the branching parameters. However, heavier stiffeners with increased thickness are required for traditional orthogonally stiffened plates, especially to yield higher fundamental frequencies. Such observations remain valid for different plate aspect ratios also. Conclusion: The geometric anisotropy of the branched layouts causes such modification of the overall stiffness distributions of the stiffened plates. Thus, the newly proposed branched layouts efficiently alter the dynamic responses at reduced mass allocations compared to the orthogonal arrangements. [ABSTRACT FROM AUTHOR]

Logo detection is a technology that identifies logos in images and returns their locations. With logo detection technology, brands can check how often their logos are displayed on social media platforms and elsewhere online and how they appear. It has received a lot of attention for its wide applications across different sectors, such as brand identity protection, product brand management, and logo duration monitoring. Particularly, logo detection technology can offer various benefits for companies to help brands measure their logo coverage, track their brand perception, secure their brand value, increase the effectiveness of their marketing campaigns and build brand awareness more effectively. However, compared with the general object detection, logo detection is more challenging due to the existence of both small logo objects and large aspect ratio logo objects. In this paper, we propose a novel approach, named Discriminative Semantic Feature Pyramid Network with Guided Anchoring (DSFP-GA), which can address these challenges via aggregating the semantic information and generating different aspect ratio anchor boxes. More specifically, our approach mainly consists of two components, namely Discriminative Semantic Feature Pyramid (DSFP) and Guided Anchoring (GA). The former is proposed to fuse semantic features into low-level feature maps to obtain discriminative representation of small logo objects, while the latter is further integrated into DSFP to generate large aspect ratio anchor boxes for detecting large aspect ratio logo objects. Extensive experimental results on four benchmarks demonstrate the effectiveness of the proposed DSFP-GA. Moreover, we further conduct visual analysis and ablation studies to illustrate the strength of the proposed DSFP-GA when detecting both small logo objects and large aspect logo objects. [ABSTRACT FROM AUTHOR]

The spread of social networking services has created an increasing demand for selecting, editing, and generating impressive images. This trend increases the importance of evaluating image aesthetics as a fundamental function of automatic image processing. However, most existing methods for aesthetics score prediction require image rescaling for input, which can affect the prediction, especially for images with unusual aspect ratios. We propose a multi-patch method, called a multi-patch aggregation network (MPA-Net), to predict image aesthetics scores by maintaining the original aspect ratios of the contents in the images. One of our key contributions is the adoption of an equal-interval multi-patch selection approach for the prediction of the aesthetics score. The effectiveness of our strategy is shown through experiments involving the large-scale AVA dataset. Our MPA-Net outperformed the reported scores of the baseline methods and achieved a better performance in terms of the mean square error (MSE) than the state-of-the-art end-to-end continuous aesthetics score prediction methods. Most notably, MPA-Net yields a significantly lower MSE particularly for images with aspect ratios far from 1.0, indicating that MPA-Net is useful for a wide range of image aspect ratios. Moreover, MPA-Net has several benefits for training and evaluation procedures. MPA-Net meets the conditions of end-to-end learning and mini-batch learning simultaneously, and MPA-Net uses only images that do not require external information during the training or prediction stages. Thus, our easy-to-handle method improves the prediction of image aesthetics scores, outstandingly for images with extraordinary aspect ratios. [ABSTRACT FROM AUTHOR]

• Discussion on an emerging rotary compressor technology. • Viability of the low-cost circolimaçon embodiment of limaçon technology is studied. • A nonlinear three degree of freedom vibration model is used to describe seal dynamics. • A leakage model considering the inertia and viscous effects is presented. • Effect of geometric parameter on seal dynamics is investigated. As an emerging technology, the limaçon rotary compressor possesses great potential for fluid-processing applications. However, the technology and associated cost required to fabricate the limaçon machine could sometimes be beyond the capability of some manufacturers. To reduce the production cost, circolimaçon embodiment whose rotor and housing are constructed of circular arcs has been proposed. This paper is intended to investigate the viability of the circolimaçon embodiment of limaçon technology based on sealing performance. A nonlinear three-degree of freedom model is presented to describe the dynamic behaviour of the apex seal during the machine operation. Additionally, the leakage through the seal-housing gap is formulated by considering the inertia and viscous effects on the flow. A numerical illustration is offered to compare the performance of the circolimaçon embodiment with that of the limaçon-to-limaçon (L2L) type machine at different pressure ratios and operating speeds. The effect of limaçon aspect ratio on the apex seal dynamics is also investigated. Based on the results, it is found that the circolimaçon embodiment exhibits comparable performance to the L2L-type machine, despite having more significant seal vibrations. The differences in the volumetric and isentropic efficiencies between the two machines are found within 8 % and 3 % , respectively. Additionally, it is also discovered that the circolimaçon compressor with a small capacity undergoes lower level of seal dynamics, suggesting a better machine reliability. [ABSTRACT FROM AUTHOR]

This article provides an analysis of the narrative and visual aspects of the full-length motion picture Os Jovens Baumann, directed by Bruna Carvalho Almeida and released in 2019. The film establishes a dialog/contrast between three narrative elements that blend together: narration (fable making), VHS found footage images, and images in the 16:9 aspect ratio. The analysis method encompasses both image formats used in the film, as well as its narration and dialog. We believe that, in this work, this separation shows that the process of investigation through image-based (archaeological) and narrative-based (fable making) layers is more important than unveiling the mystery surrounding the disappearance of the young Baumanns. [ABSTRACT FROM AUTHOR]

The water entry, crown formation, and cavity dynamics of gravity-driven thick solid and annular disks were studied by conducting a series of detailed laboratory experiments. Three different release conditions were examined to study the fully guarded and partially guarded release conditions on the cavity dynamics. The effects of aspect ratio, geometry, and density of disks on crown formation, interface impact, seal development, and air entrainment were investigated. Four crown shapes were identified, and the crown structure was classified based on the dimensionless moment of inertia, I*, and the impact Froude number, Fro. The effects of controlling parameters on variations of crown dimensions were investigated. It was found that the normalized crown diameter decreased with I* and crown geometries were found to be smaller in annular disks. Experimental observations have shown that high-density disks have larger pinch-off depth and form a deep seal in the ambient water. The normalized pinch-off depth increased with Fro and the normalized pinch-off depth in cylindrical thick disks were smaller than thin disks and spherical objects. The temporal variations of cavity indicated a non-linear correlation between the growth rate of normalized pinch-off depth and time. The disk's velocities in three different stages were measured, it was found that the settling velocities followed a linear relationship with I*, and it was affected by the release conditions. The velocity and vortex fields were extracted from the particle image velocimetry data. The velocity fields showed that the solid disks affected a greater surrounding ambient in comparison to the annular disks due to their higher initial momentum. Periodic vortex shedding was formed in the wake of annular disks, and the frequency of the vortex field was found to be proportional to the disk density. [ABSTRACT FROM AUTHOR]

In this study, h -Boron Nitride (h-BN) nanosheets have been successfully exfoliated using Bougainvillea flower extract and ultrasonication process. The microstructural parameters such as microstrain, stress, and energy density were calculated using Williamson–Hall analysis. The time of ultrasonication and post-annealing temperature significantly alter the microstructure parameters such as crystal size, microstrain, stress, and energy density upon sonication. The lattice constants such as a -axis and c -axis, cell volume, and bond length did not alter significantly which confirms that there is no crystal structure deformation upon sonication and annealing processes. FESEM result revealed that exfoliation of h-BN and ultrasonication duration greatly influenced the exfoliation. Raman and FTIR studies confirm the pure phase of h-BN without secondary impurities. A simple ultrasonication process would effectively exfoliate h-BN layers and this could be used for tribological applications to reduce the friction between rubbing surfaces. • h-BN nanosheets were exfoliated using Bougainvillea flower extract and ultrasonication. • Peak broadening was analysed using Williamson-Hall method. • Crystallite size, microstrain, stress, and energy density are significantly altered. • FESEM images & Aspect Ratio results revealed that reduction of number of layers upon sonication. • FTIR and Raman spectral analysis revealed vibration modes of h-BN, confirm that there are no impurities present in the samples. [ABSTRACT FROM AUTHOR]

Silicon carbide is a key material in microwave (MW) processing and is widely employed as an MW absorber and catalytic agent. In this study, we experimentally improve the MW absorption properties of SiC particles at frequencies of 2.45 GHz by considering the shapes of fibers. We heated SiC fibers using MW radiation and their heating behaviors were then compared with their MW absorption properties. The heating behavior of the fibers was dependent on their aspect ratio; this result was in accordance with theoretical predictions. Furthermore, the mass ratio dependence of the MW heating behavior was investigated by employing an Al2O3-SiC fiber mixture. Their absorption properties exhibited peak MW absorption at specific relative densities under Emax and indicated different behavior from that predicted by the Maxwell–Garnett equation. [ABSTRACT FROM AUTHOR]

An efficient finite element model based on conventional shell elements available in ABAQUS software has been developed for free vibration analysis of FGM monolayer and sandwich flat panels. Applications to sandwich plates with an FGM layer embedded as either a core or face sheets with different through-the-thickness material gradations in the form of power (P-FGM), sigmoid (S-FGM), and exponential (E-FGM) distributions are considered. A user-defined material subroutine UMAT was used to implement functionally graded properties of the constitutive FGM layer. Numerical studies are given for free vibrations of monolayer and sandwich plates subjected to various boundary conditions and with different structural parameters such as span-to-thickness, face sheet-core-face sheet thickness and aspect ratios, and volume fraction index. The studies showed very good agreement between the present results and those existing in the literature that confirmed the accuracy of the developed model at minimal computational costs. This emphasizes the efficiency of the shell-based modeling strategy proposed for the calculations. A series of numerical solutions obtained in the research extends the results of known testing examples and may serve as additional benchmark data for other analysts. [ABSTRACT FROM AUTHOR]

We introduce a new machine learning approach to retrieve cloud optical thickness (COT) fields from visible passive imagery. In contrast to the heritage independent pixel approximation (IPA), our convolutional neural network (CNN) retrieval takes the spatial context of a pixel into account and thereby reduces artifacts arising from net horizontal photon transfer, which is commonly known as independent pixel (IP) bias. The CNN maps radiance fields acquired by imaging radiometers at a single wavelength channel to COT fields. It is trained with a low-complexity and therefore fast U-Net architecture with which the mapping is implemented as a segmentation problem with 36 COT classes. As a training data set, we use a single radiance channel (600 nm) generated from a 3D radiative transfer model using large eddy simulations (LESs) from the Sulu Sea. We study the CNN model under various conditions based on different permutations of cloud aspect ratio and morphology, and we use appropriate cloud morphology metrics to measure the performance of the retrievals. Additionally, we test the general applicability of the CNN on a new geographic location with LES data from the equatorial Atlantic. Results indicate that the CNN is broadly successful in overcoming the IP bias and outperforms IPA retrievals across all morphologies. Over the Atlantic, the CNN tends to overestimate the COT but shows promise in regions with high cloud fractions and high optical thicknesses, despite being outside the general training envelope. This work is intended to be used as a baseline for future implementations of the CNN that can enable generalization to different regions, scales, wavelengths, and sun-sensor geometries with limited training. [ABSTRACT FROM AUTHOR]

The demand for measuring the feature size of small holes is increasing. In this study, a nanometer precision height gauge for small holes with a high aspect ratio was developed. The height gauge comprises a Michelson interferometer, a moving and weight balance system, a tactile probing system, and a positioning control system. The errors due to the angular deflection of the moving stage were analyzed, and a corner-cube-based interferometer was designed. The structural design of the moving and weight balance systems was explored. A tactile micro/nano-probing system was developed and used. The signal processing, positioning control and measurement strategy, and software were developed. The system was calibrated and verified by testing the reference ring gauge. The experimental results show that the height gauge can achieve a resolution of 5 nm, a measurement uncertainty of 90 nm (k = 2), and a measuring range of 30 mm. The prototype can be used to measure the depth of small holes with a high aspect ratio. • This study presentes a nanometer precision height gauge for small holes with high aspect ratio by using a tactile probe. • Micro-hole and micro-groove depth can be measured with nanometer accuracy. • A moving and weight balance system is designed. [ABSTRACT FROM AUTHOR]

A radial flow cell (RFC, inlet tube radius 0.95 mm and gaps of 1-4.2 mm) was used to investigate the removal of two thin (80-230 pm thick) model soil layers from glass and 304 stainless steel substrates by the flow of water at 20 °C. Under the flow conditions employed (Reynolds numbers 200-1400), inertial effects give rise to recirculation zones and regions of high shear stress on the bottom, soiled plate. The soils were dried layers of (i) instant coffee and (ii) a domestic abrasive cleaning suspension comprising fine particulates in a soluble matrix. Cleaning data exhibited a constant local erosion rate which varied strongly with radial position. For both soils, cleaning involved the growth of a circular cleaned region and redeposition of particulate matter in a ring at locations close to the foot of the secondary recirculation zone predicted by 2D axisymmetric CED simulations. Removal beyond this location was observed with the coffee layers, indicating that cleaning for this soil was controlled primarily by simple diffusion mechanisms. The effect of channel aspect ratio and flow rate on the location of recirculation zones and shear stress distributions was investigated. The local cleaning rate in these steady flows was not linked simply to local wall shear stress. [ABSTRACT FROM AUTHOR]

A recently developed conservative level set model, coupled with the Navier-Stokes equations, was invoked to simulate non-spherical droplet impact in three dimensions. The advection term in the conservative level set model was tackled using the traditional central difference scheme on a half-staggered grid. The pressure velocity coupling was decoupled using the projection method. The inhouse code was written in Fortran and was run with the aid of the shared memory parallelism, OpenMP. Before conducting extensive simulations, the model was tested on meshes of varied resolutions and validated against experimental works, with satisfyingly qualitative and quantitative agreement obtained. The model was then employed to predict the impact and splashing dynamics of non-spherical droplets, with the focus on the effect of the aspect ratio. An empirical correlation of the maximum spread factor was proposed. Besides, the number of satellite droplets when splashing occurs was in reasonable agreement with a theoretical model. [ABSTRACT FROM AUTHOR]