Target Size Research Articles

Damage and Failure Modeling of Composite Material Structures Using the Pam-Crash Code

Simulating composite material structures requires complex constitutive models, which normally require fine meshes to obtain an accurate prediction of their behavior. Pam-Crash software has been used for several years in the automotive industry and has been proved to be an efficient tool for simulating metallic structures, returning good correlations in a fast computational time. However, constitutive models for composite materials in Pam-Crash present some difficulties: some materials are not able to be suitably modeled and the predictive results depend on the mesh refinement. This work proposes a solution for predicting the progressive damage of composite materials in Pam-Crash, which scales the energy dissipated by the damage mechanisms and checks the viability of modeling the material behavior, taking into account the recommended size of finite elements in the automotive industry. The proposed solution is applied for the simulation of Open Hole specimens to evaluate the ultimate strength consistency. After this, it is applied for the simulation of Compact Tension specimens to check the consistency of crack propagation behavior. By considering the target size of the finite elements in the material card definition, the predictions demonstrate great improvement in the equivalence in results between different mesh refinements. Finally, the solution is applied to simulate impact tests on large structures. Good correlations with experimental data are obtained in fast computational times, making this methodology a candidate for application in composite-related automotive simulations.

Semantic segmentation network for mangrove tree species based on UAV remote sensing images

Mangroves are special vegetation that grows in the intertidal zone of the coast and has extremely high ecological and environmental value. Different mangrove species exhibit significant differences in ecological functions and environmental responses, so accurately identifying and distinguishing these species is crucial for ecological protection and monitoring. However, mangrove species recognition faces challenges, such as morphological similarity, environmental complexity, target size variability, and data scarcity. Traditional mangrove monitoring methods mainly rely on expensive and operationally complex multispectral or hyperspectral remote sensing sensors, which have high data processing and storage costs, hindering large-scale application and popularization. Although hyperspectral monitoring is still necessary in certain situations, the low identification accuracy in routine monitoring severely hinders ecological analysis. To address these issues, this paper proposes the UrmsNet segmentation network, aimed at improving identification accuracy in routine monitoring while reducing costs and complexity. It includes an improved lightweight convolution SCConv, an Adaptive Selective Attention Module (ASAM), and a Cross-Layer Feature Fusion Module (CLFFM). ASAM adaptively extracts and fuses features of different mangrove species, enhancing the network’s ability to characterize mangrove species with similar morphology and in complex environments. CLFFM combines shallow details and deep semantic information to ensure accurate segmentation of mangrove boundaries and small targets.Additionally, this paper constructs a high-quality RGB image dataset for mangrove species segmentation to address the data scarcity problem. Compared to traditional methods, our approach is more precise and efficient. While maintaining relatively low parameters and computational complexity (FLOPs), it achieves excellent performance with mIoU and mPA metrics of 92.21% and 95.98%, respectively. This performance is comparable to the latest methods using multispectral or hyperspectral data but significantly reduces cost and complexity. By combining periodic hyperspectral monitoring with UrmsNet-supported routine monitoring, a more comprehensive and efficient mangrove ecological monitoring can be achieved.These research findings provide a new technical approach for large-scale, low-cost monitoring of important ecosystems such as mangroves, with significant theoretical and practical value. Furthermore, UrmsNet also demonstrates excellent performance on LoveDA, Potsdam, and Vaihingen datasets, showing potential for wider application.

EC-PFN: a multiscale woven fusion network for industrial product surface defect detection

In order to address challenges such as small target sizes, low contrast, significant intra-class variations, and indistinct inter-class differences in surface defect detection, this paper proposes the Enhanced Context-aware Parallel Fusion Network (EC-PFN). The network employs a Featur Weave Network architecture to enhance contextal awareess and parallel fusion capabilities. It utilizes a Feature Fusion Module (UniFusionLayer) for effective multiscale and multisemantic feature learning, offering new perspectives on feature fusion. Additionally, a Receptive Field Block (RFB) module is introduced to expand the receptive field, enhancing feature extraction in scenarios with low contrast and subtle defects. The Loss Ranking Module (LRM) is incorporated to optimize the target-oriented loss, improving performance by omitting low-confidence bounding boxes. Extensive experiments on a light guide plate defect dataset demonstrate that EC-PFN achieves a detection accuracy (mAP) of 98.9%, a detection speed of 92 FPS, and a computational cost of 14.5 GFLOPs, outperforming mainstream surface defect detection models.

Mass granulation of Al-promoted CaO-based sorbent via moulding-crushing methods for cyclic CO2 capture

Calcium looping (CaL) process, as an effective way to achieve CO2 mitigation from high-temperature flue gas streams, is one of the most promising alternatives to amine scrubbing (a well-established technology for industrial post-combustion CO2 capture). CaO sorbent is considered to be an ideal CO2 adsorption material. Moreover, the development of granulation/pelletization techniques along with the mass preparation of the CaO-based sorbent is imperative for realistic large-scale applications. This work proposes two practicable moulding-crushing techniques for the scale-up granulation of CaO-based sorbents, in which the kilogram-scale produced Al-promoted CaO-based sorbent powders were first moulded and subsequently crushed into the granules of target sizes. Three types of organic acids–acetic acid, citric acid and malonic acid were employed as peptizing agents to optimize the granulation process. As a result, the anti-attrition properties and compressive strength of the synthetic sorbents were elevated owing to the introduction of an appropriate amount of acetic or malonic acid, for it expedited the disintegration of the pseudo-boehmite (served as binder agent) particles into sol particles, which allowed for tighter bonding of sorbent particles. In addition, corncob powder acted as a pore-forming agent, enhancing the porous structure of the sorbent particles due to the gases released from the thermal decomposition of organic groups during calcination. Nevertheless, the results revealed that the porous and loose structure adversely affected the mechanical strength of the granules.

DIEN: A dual-factor iterative enhancement network with the global Re-calibration feature for coronary artery segmentation

ObjectiveCoronary artery segmentation is a prerequisite for assessing the severity of coronary artery disease (CAD), which has a high prevalence and mortality rate. However, segmentation of the coronary arteries is challenging due to their complex anatomy diverse morphology, and small target size. MethodsThis study proposes a dual-factor iterative enhancement network (DIEN) for automatic coronary artery segmentation in coronary computed tomography angiographic (CCTA) images. The DIEN includes a dual-factor layer-by-layer iterative convolution (DLIC) module and a global recalibration feature selection (GRFS) module. The DLIC in the encoding part aims to capture diverse semantic information from coronary arteries with different morphologies based on extracting multiple features under different receptive fields. The GRFS bridges the semantic gap between the encoding and decoding parts by exploiting the global information of the slices to improve the feature representation of the coronary artery. ResultsExperiments show that the DIEN outperforms seven state-of-the-art algorithms on a variety of metrics on two datasets. On a self-collected dataset, the DIEN achieved a 1.01 %, 0.69 %, 1.91 %, and 0.63 % improvement over U-Net in IoU, dice, recall, and precision, respectively. In comparison to U-Net, the IoU, dice, recall, and precision obtained by the DIEN on the public dataset were improved by 1.03 %, 0.73 %, 0.12 %, and 0.23 %, respectively. ConclusionCompared with state-of-the-art approaches, the proposed model achieves good performance in segmenting coronary arteries both on our dataset and a publicly available dataset. Significance: Our method can accurately segment coronary arteries, and can contribute to improved diagnosis and assessment of CAD.

A multimodal framework for pepper diseases and pests detection

Pepper diseases and pests typically exhibit small target proportions, diverse shapes and sizes, complex imaging backgrounds, and similarities with the background. Existing detection methods perform poorly in identifying targets of different sizes and shapes within the same scene, and they lack adequate noise suppression capabilities. To address the practical needs of detecting pepper diseases and pests in complex scenarios, we have constructed the first multimodal pepper diseases and pests object detection dataset (PDD). This dataset includes a wide variety of diseases and pests images, along with detailed natural language descriptions of their attributes. Locating the described targets in complex scenes with similar disease symptoms and leaf occlusion presents a significant challenge. To tackle this issue, we propose the PepperNet model for object detection in pepper diseases and pests images using natural language descriptions. This model decomposes complex multimodal features of language and images into explicit attribute features and employs fine-grained multimodal attribute contrast learning strategies. This approach effectively distinguishes subtle local differences between similar objects, achieving fine-grained mapping from language to vision in complex scenarios. Our detection results show a mAP@0.5 of 91.93% and a detection speed of 121.8 frames per second. Visualizations indicate that the model maintains high robustness under varying noise levels and occlusion conditions, demonstrating superior performance and stability across diverse complex scenarios.

Size Matters: Characterizing the Effect of Target Size on Wi-Fi Sensing Based on the Fresnel Zone Model

Size Matters: Characterizing the Effect of Target Size on Wi-Fi Sensing Based on the Fresnel Zone Model

BFAR: improving radar odometry estimation using a bounded false alarm rate detector

This work introduces a novel detector, bounded false-alarm rate (BFAR), for distinguishing true detections from noise in radar data, leading to improved accuracy in radar odometry estimation. Scanning frequency-modulated continuous wave (FMCW) radars can serve as valuable tools for localization and mapping under low visibility conditions. However, they tend to yield a higher level of noise in comparison to the more commonly employed lidars, thereby introducing additional challenges to the detection process. We propose a new radar target detector called BFAR which uses an affine transformation of the estimated noise level compared to the classical constant false-alarm rate (CFAR) detector. This transformation employs learned parameters that minimize the error in odometry estimation. Conceptually, BFAR can be viewed as an optimized blend of CFAR and fixed-level thresholding designed to minimize odometry estimation error. The strength of this approach lies in its simplicity. Only a single parameter needs to be learned from a training dataset when the affine transformation scale parameter is maintained. Compared to ad-hoc detectors, BFAR has the advantage of a specified upper-bound for the false-alarm probability, and better noise handling than CFAR. Repeatability tests show that BFAR yields highly repeatable detections with minimal redundancy. We have conducted simulations to compare the detection and false-alarm probabilities of BFAR with those of three baselines in non-homogeneous noise and varying target sizes. The results show that BFAR outperforms the other detectors. Moreover, We apply BFAR to the use case of radar odometry, and adapt a recent odometry pipeline, replacing its original conservative filtering with BFAR. In this way, we reduce the translation/rotation odometry errors/100 m from 1.3%/0.4∘ to 1.12%/0.38∘, and from 1.62%/0.57∘ to 1.21%/0.32∘, improving translation error by 14.2% and 25% on Oxford and Mulran public data sets, respectively.

Impact of skin tone on target size detectability in photoacoustic breast imaging

Impact of skin tone on target size detectability in photoacoustic breast imaging

Nondestructive perch target detection and size measurement from RGB-D images in recirculating aquaculture system

Nondestructive perch target detection and size measurement from RGB-D images in recirculating aquaculture system

Identification of a postnatal period of interdependent neurogenesis and apoptosis in peripheral neurons.

During neurogenesis, excessive numbers of neurons are produced in most regions of the central and peripheral nervous systems. Nonessential neurons are eliminated by apoptosis, or programmed cell death. This has been most thoroughly characterized in the peripheral nervous system (PNS) where targets of innervation play a key role in this process. As maturing neurons project axons towards their targets of innervation, they become dependent upon these targets for survival. Survival factors, also called neurotrophic factors, are produced by targets, inhibit apoptosis cascades, and promote further growth and differentiation. Because neurotrophic factors are limited, as is target size, neurons that do not correctly and efficiently innervate targets undergo apoptosis ( Levi-Montalcini, 1987; Davies, 1996). Thus, excessive neurogenesis acts to ensure that sufficient numbers of neurons are produced during development. In the superior cervical ganglion (SCG), this process of neurogenesis and subsequent apoptosis is reported to be complete by postnatal day 3-4 (P3-P4) in mice. Surprisingly, we observed significant numbers of apoptotic neurons out to P14, and neurogenesis was still present at P14 as well. In both the SCG and geniculate ganglion (GG), postnatal neurogenesis was dependent on apoptosis because little or no postnatal neurogenesis was observed in Bax-/- mice, in which apoptosis is eliminated. These results indicate that both neurogenesis and apoptosis continue to occur well after birth in peripheral ganglia, and that neurogenesis depends on apoptosis, suggesting that neurogenesis continues postnatally to replace neurons that are eliminated during synaptic refinement.

Enhanced YOLOv8-Based Model with Context Enrichment Module for Crowd Counting in Complex Drone Imagery

Crowd counting in aerial images presents unique challenges due to varying altitudes, angles, and cluttered backgrounds. Additionally, the small size of targets, often occupying only a few pixels in high-resolution images, further complicates the problem. Current crowd counting models struggle in these complex scenarios, leading to inaccurate counts, which are crucial for crowd management. Moreover, these regression-based models only provide the total count without indicating the location or distribution of people within the environment, limiting their practical utility. While YOLOv8 has achieved significant success in detecting small targets within aerial imagery, it faces challenges when directly applied to crowd counting tasks in such contexts. To overcome these challenges, we propose an improved framework based on YOLOv8, incorporating a context enrichment module (CEM) to capture multiscale contextual information. This enhancement improves the model’s ability to detect and localize tiny targets in complex aerial images. We assess the effectiveness of the proposed framework on the challenging VisDrone-CC2021 dataset, and our experimental results demonstrate the effectiveness of this approach.

Improved extraction efficiency of radioactive copper produced via accelerator neutrons method through phosphate buffer-enhanced column pre-treatment

We report a straightforward and robust method for isolating medical copper radioisotopes 64Cu and 67Cu, generated by an accelerator neutrons technique from natZn(n, x). This study reveals the key role of a phosphate buffer pre-treatment of the cation exchange column in the separation process. Incorporating the phosphate buffer into the column pre-treatment markedly enhances the retention of copper isotopes within the column throughout the separation procedure. This approach yields a remarkably high-purity radioactive copper sample with a high extraction efficiency of 94.4 (1.5) % of the initially produced copper, all within a relatively short experimental timeframe of approximately 5 h for 100 g of starting material. This single-step separation scheme is reproducible across a range of starting material target sizes, from small (10 g) to large (100 g). The copper radioisotopes obtained are suitable for use in pre-clinical studies. Thus, this approach offers a more effective means for routine preparation of copper radioisotopes.

YOLOX-S-TKECB: A Holstein Cow Identification Detection Algorithm

Accurate identification of individual cow identity is a prerequisite for the construction of digital farms and serves as the basis for optimized feeding, disease prevention and control, breed improvement, and product quality traceability. Currently, cow identification faces challenges such as poor recognition accuracy, large data volumes, weak model generalization ability, and low recognition speed. Therefore, this paper proposes a cow identification method based on YOLOX-S-TKECB. (1) Based on the characteristics of Holstein cows and their breeding practices, we constructed a real-time acquisition and preprocessing platform for two-dimensional Holstein cow images and built a cow identification model based on YOLOX-S-TKECB. (2) Transfer learning was introduced to improve the convergence speed and generalization ability of the cow identification model. (3) The CBAM attention mechanism module was added to enhance the model’s ability to extract features from cow torso patterns. (4) The alignment between the apriori frame and the target size was improved by optimizing the clustering algorithm and the multi-scale feature fusion method, thereby enhancing the performance of object detection at different scales. The experimental results demonstrate that, compared to the traditional YOLOX-S model, the improved model exhibits a 15.31% increase in mean average precision (mAP) and a 32-frame boost in frames per second (FPS). This validates the feasibility and effectiveness of the proposed YOLOX-S-TKECB-based cow identification algorithm, providing valuable technical support for the application of dairy cow identification in farms.

Large-Kernel Central Block Masked Convolution and Channel Attention-Based Reconstruction Network for Anomaly Detection of High-Resolution Hyperspectral Imagery

In recent years, the rapid advancement of drone technology has led to an increasing use of drones equipped with hyperspectral sensors for ground imaging. Hyperspectral data captured via drones offer significantly higher spatial resolution, but this also introduces more complex background details and larger target scales in high-resolution hyperspectral imagery (HRHSI), posing substantial challenges for hyperspectral anomaly detection (HAD). Mainstream reconstruction-based deep learning methods predominantly emphasize spatial local information in hyperspectral images (HSIs), relying on small spatial neighborhoods for reconstruction. As a result, large anomalous targets and background details are often well reconstructed, leading to poor anomaly detection performance, as these targets are not sufficiently distinguished from the background. To address these limitations, we propose a novel HAD network for HRHSI based on large-kernel central block masked convolution and channel attention, termed LKCMCA. Specifically, we first employ the pixel-shuffle technique to reduce the size of anomalous targets without losing image information. Next, we design a large-kernel central block masked convolution to make the network pay more attention to the surrounding background information, enabling better fusion of the information between adjacent bands. This, coupled with an efficient channel attention mechanism, allows the network to capture deeper spectral features, enhancing the reconstruction of the background while suppressing anomalous targets. Furthermore, we introduce an adaptive loss function by down-weighting anomalous pixels based on the mean absolute error. This loss function is specifically designed to suppress the reconstruction of potentially anomalous pixels during network training, allowing our model to be considered an excellent background reconstruction network. By leveraging reconstruction error, the model effectively highlights anomalous targets. Meanwhile, we produced four benchmark datasets specifically for HAD tasks using existing HRHSI data, addressing the current shortage of HRHSI datasets in the HAD field. Extensive experiments demonstrate that our LKCMCA method achieves superior detection performance, outperforming ten state-of-the-art HAD methods on all datasets.

Three-dimensional endoscopic imaging system based on micro-lithography mask structured light projection

To achieve effective in-situ endoscopic diagnosis and treatment, the measurement of the size of lesions (such as tumors) and the characterization of their shape are important. However, the application of binocular endoscopy is still limited due to issues such as the lack of texture in some scenes, difficulty in matching, and large computational load. To address this, we have developed a 3D endoscopic imaging system based on micro-lithography mask structured light projection to measure the shape and size of targets within the endoscopic view. Firstly, a brand new mechanical design was implemented for the endoscope tip to integrate both white light and structured light channels. Then, a projection lens based on Q-type aspheric design and a micro-lithography mask based on the M-array were designed to achieve high contrast and high-resolution structured light projection in the endoscopic scene. Finally, by identifying feature points in the target and reference images, pixel matching and disparity calculation were achieved, allowing for 3D reconstruction. Our proposed 3D endoscopic imaging system was validated in a gastric model and a cervical model, where the model was reconstructed and compared with the ground truth, yielding mean RMSE of 0.20–0.31 mm at a working distance of about 40 mm, thus confirming the effectiveness of our system.

Recruiting and retaining healthcare workers in Scotland to a longitudinal COVID-19 study: a descriptive analysis

BackgroundRapid timescales for the design and delivery of research were common during the COVID-19 pandemic. The recruitment and retention of healthcare workers (HCWs) as participants in research studies are notoriously challenging, but this was exacerbated during the pandemic by the unprecedented demand placed on the workforce. The SARS-CoV-2 Immunity and Reinfection Evaluation (SIREN study) is a prospective multicentre cohort study following HCWs in the UK. This paper discusses the strategies and challenges associated with recruitment and retention of HCW participants in Scotland.MethodsThere were 44,546 HCWs recruited to the SIREN study, of whom 6,285 were recruited by research teams at ten different research sites in Scotland between October 2020 and March 2021. Information on target and actual sample size, availability of resource, recruitment rate, and recruitment and engagement strategies by site was collated from SIREN study documentation and discussions with local key SIREN site staff. Individual-level data from 6,153 HCW participants with ongoing consent for all data usage were also collated, including socio-demographic data and information on withdrawal (in first year) and opt-in to a study extension after one year. Factors associated with these outcomes were explored in logistic regression analyses.ResultsDifferent recruitment strategies were used in each site according to local agreements, protocol and staff capacity, with the recruitment period ranging from 13 to 160 days. The locally-agreed recruitment target was met in four sites. The proportion of participants who withdrew in the first year ranged from 3.1 to 24.8% by site, while subsequent opt-in to a 12-month study extension ranged from 28.6 to 74.8%. The sites with the highest proportions of withdrawals were the same four sites with lowest proportions of opt-in. On an individual level, there was a lower level of retention among younger participants, and those from lower socio-economic backgrounds and minority ethnic groups.ConclusionsSite-specific factors including research-readiness likely had a significant influence on recruitment and retention, more so than the specific recruitment or retention strategies employed. Independent of site factors, individual-level variables influenced recruitment and retention, suggesting targeted strategies may be needed to promote research engagement among particular socio-demographic groups.

Investigating Object Translation in Room-scale, Handheld Virtual Reality.

Handheld devices have become an inclusive alternative to head-mounted displays in virtual reality (VR) environments, enhancing accessibility and allowing cross-device collaboration. Object manipulation techniques in 3D space with handheld devices, such as those in handheld augmented reality (AR), have been typically evaluated in table-top scale and we currently lack an understanding of how these techniques perform in larger scale environments. We conducted two studies, each with 30 participants, to investigate how different techniques impact usability and performance for room-scale handheld VR object translations. We compared three translation techniques that are similar to commonly studied techniques in handheld AR: 3DSlide, VirtualGrasp, and Joystick. We also examined the effects of target size, target distance, and user mobility conditions (stationary vs. moving). Results indicated that the Joystick technique, which allowed translation in relation to the user's perspective, was the fastest and most preferred, without difference in precision. Our findings provide insights for designing room-scale handheld VR systems, with potential implications for mixed reality systems involving handheld devices.

Effects of grain size, dislocation density, and texture type on the etching behavior of Cu target in magnetron sputtering process

Abstract Copper thin film is commonly applied in the microelectronics and battery industries, and environmentally friendly magnetron sputtering process is one of the main coating processes. The effects of grain size, dislocation density, and texture type of Cu target on sputtering efficiency were researched in this work. High-purity copper cast ingots were rolled into plates. Pieces were then cut from different surfaces of the plates and assembled into the targets named as Target 1, Target 2, and Target 3 for magnetron sputtering. To investigate the etching behavior of the three targets, EBSD technology was utilized for analysis, and the micro-morphologies of the etching grooves were observed using SEM. After the sputtering process, the weight loss rate and etching depth of the various targets were measured to compare the respective sputtering efficiency. Target 1 exhibited the highest sputtering weight loss rate at any time period compared to Target 2 and Target 3, and reached its maximum depth after every 15 minutes of sputtering. In the first 45 minutes, the etching depth of Target 2 was slightly greater than that of Target 3. While the etching depth of Target 3 increased significantly in the last 15 minutes compared to that of Target 2. The most significant factor influencing the sputtering efficiency of the target material was grain size, with finer grains leading to higher sputtering efficiency. The dislocation density had a greater impact in the first 30 minutes, but the texture type became more important as the sputtering process continued.

Infrared Small Target Detection Based on Weighted Improved Double Local Contrast Measure

The robust detection of infrared small targets plays an important role in infrared early warning systems. However, the high-brightness interference present in the background makes it challenging. To solve this problem, we propose a weighted improved double local contrast measure (WIDLCM) algorithm in this paper. Firstly, we utilize a fixed-scale three-layer window to compute the double neighborhood gray difference to screen candidate target pixels and estimate the target size. Then, according to the size information of each candidate target pixel, an improved double local contrast measure (IDLCM) based on the gray difference is designed to enhance the target and suppress the background. Next, considering the structural characteristics of the target edge, we propose the variance-based weighting coefficient to eliminate clutter further. Finally, the targets are detected by an adaptive threshold. Extensive experimental results demonstrate that our method outperforms several state-of-the-art methods.