Single-stage Method Research Articles

KORELASI PERILAKU HIDUP BERSIH DAN SEHAT DENGAN KECACINGAN PADA SISWA SEKOLAH DASAR DI KABUPATEN PROBOLINGGO

Background: Soil-transmitted worms are still a health problem in Probolinggo Regency, especially related to clean and healthy living behavior. Health and hygiene education is an effort to reduce worm transmission and infection. The purpose of this study was to identify clean and healthy living behaviors related to worms. Method: The study design was observational analytic with a Cross-Sectional Study approach. The population was elementary school students in Probolinggo Regency with a sample size of 607 students taken by cluster sampling with a single-stage method. Identification was carried out on clean and healthy living behaviors and the number of worm eggs in feces and soil. Data analysis used Chi Square with an alpha of 0.05. Results: The data showed that 96.4% of respondents were negative and 3.6% were positive with a distribution of worm eggs of 2.1% Ascaris lumbricoides, 1.3% Hookworm and 0.5% Trichuris trichiura with mild infection intensity. The results of the Chi Square test showed no relationship between washing hands before eating, washing hands after defecating, wearing footwear, and nail conditions with worms in students with p values of 0.748, 0.584, 0.550, 0.777, respectively. Conclusion: These results are due to the history of exposure and the body's immunity in overcoming parasite invasion. Long exposure to children with positive worms will make the parasite colony maintain a clean and healthy lifestyle. Conversely, low exposure and strong immunity keep children free from worm infections.

A Step-by-Step Surgical Technique to the Posterior Auricular "Revolving Door" Flap for Conchal Tumor Excision: A Guide for the Surgical and Dermatological Trainee.

Skin cancer is the most common malignancy worldwide and the auricle is particularly vulnerable to the effects of cumulative sun exposure. Reconstruction of auricular defects after skin cancer excision remains a vexing surgical challenge owing to the complex three-dimensional- anatomy of the external ear, particularly in the conchal bowl. The postauricular island "revolving door" flap is a reliable single-stage method for reconstructing anterior auricular and conchal defects. While the revolving door flap is well known in the literature, a clear description of how to use the flap to reconstruct an anterior auricular conchal bowl defect is lacking. Herein, we present a step-by-step description of the surgical technique, using surgical photos of a patient who presented with basal cell carcinoma on their anterior auricular concha.

An efficient and lightweight banana detection and localization system based on deep CNNs for agricultural robots

Accurate detection and localization of fruits in natural environments is a key step for fruit picking robots to achieve precise harvesting. However, existing banana detection and positioning methods have two main limitations in practical applications: a large number of model parameters that make deployment difficult, and a need for performance improvement. To tackle the above issues, a high-precision and lightweight banana bunch recognition and localization method was proposed and deployed on edge devices for application. First, a Slim-Banana model was proposed based on the improvement of YOLOv8l. In order to reduce the model calculation amount and maintain high performance, GSConv was introduced in the Slim-Banana model to replace the standard convolution, and combined with grouped convolution and spatial convolution. At the same time, the cross-stage local network (GSCSP) module was designed to reduce the computational complexity and the complexity of the network structure through a single-stage aggregation method. Then, the RealSense depth sensor is combined with TOF technology to perform image registration and 3D localization of the banana. Finally, the pipeline is deployed on the Nvidia Orin NX edge device and its performance and resource consumption in actual work are deeply analyzed. Experimental results show that the detection precision, recall, mAP and inference time of our method are 0.947, 0.948, 0.98 and 113.6ms respectively, the network memory size required is 4449MiB, and the average localization errors in the X-axis, Y-axis and Z-axis directions are 13.47mm, 12.87mm and 13.87mm respectively. To our knowledge, this is the first work that implements banana detection and localization on edge devices. Experimental results show that compared with existing methods, our method achieves better performance in complex orchard environments, achieving efficient and lightweight banana recognition and localization.

Enhancing autonomous pavement crack detection: Optimizing YOLOv5s algorithm with advanced deep learning techniques

To enhance the safety and comfort of vehicle travel, detecting pavement cracks is a critical task in road management. This article introduces an advanced single-stage target detection method utilizing the YOLOv5s algorithm to enhance real-time performance and accuracy. Initially, Squeeze-and-Excitation Networks are integrated into the model to facilitate self-learning for improved crack characterization. Subsequently, anchors computed through the K-means clustering algorithm are closely aligned with the fracture dataset, achieving an adaptation rate of 99.9 % and enhancing the recall rate of the model. Furthermore, the inclusion of the SimSPPF module from YOLOv6 diminishes memory usage and expedites detection speed. By replacing the original nearest up-sampling method with transposed convolution, optimization of up-sampling for crack datasets is achieved. Performance assessments reveal that the refined YOLOv5s algorithm attains an F1 score of 91 %, a mean Average Precision (mAP) of 93.6 %, and a 1.54 % increase in frames per second (fps) for pavement crack detection. This enhancement in detection technology signifies a substantial advancement in the maintenance and longevity of road infrastructure.

PointStaClu: A Deep Point Cloud Clustering Method Based on Stable Cluster Discrimination

Potential inconsistencies between the goals of unsupervised representation learning and clustering within multi-stage deep clustering can diminish the effectiveness of these techniques. However, because the goal of unsupervised representation learning is inherently flexible and can be tailored to clustering, we introduce PointStaClu, a novel single-stage point cloud clustering method. This method employs stable cluster discrimination (StaClu) to tackle the inherent instability present in single-stage deep clustering training. It achieves this by constraining the gradient descent updates for negative instances within the cross-entropy loss function, and by updating the cluster centers using the same loss function. Furthermore, we integrate entropy constraints to regulate the distribution entropy of the dataset, thereby enhancing the cluster allocation. Our framework simplifies the process, employing a single loss function and an encoder for deep point cloud clustering. Extensive experiments on the ModelNet40 and ShapeNet dataset demonstrate that PointStaClu significantly narrows the performance gap between unsupervised point cloud clustering and supervised point cloud classification, presenting a novel approach to point cloud classification tasks.

MFFC-SDN: multi-level feature fusion codec-based ship detection network in SAR images

ABSTRACT In recent years, with the development of deep learning, SAR image-based ship target-detection technology has become a current research hotspot. SAR images are characterized by complex backgrounds, significant speckle noise interference, and poor interpretability. To address this challenge, this study proposes a new Gaussian circle radius determination strategy and a multi-level feature fusion codec-based ship detection network (MFFC-SDN) for SAR images. Differing from CenterNet’s corner-oriented Gaussian circle strategy, the Gaussian circle strategy proposed in this paper takes the centre point coordinates of the real target as the origin to define a circular region. This approach allows for more precise supervision of the training model. Meanwhile, we propose MFFC-SDN to solve the problem of the poor performance of CenterNet in SAR ship detection. MFFC-SDN is an anchor-free, single-stage target-detection method with a low computation cost. In MFFC-SDN, we introduce a Multilevel Feature Recapture Codec Module (MFR-CM) to enhance the feature extraction capability of the network by using the features of the encoding stage twice because of the easy loss of image features in the lengthy codec process of the CenterNet network backbone. A Residual Attention Pyramid Feature Fusion Module (RA-PFFM) is designed to enhance the feature map and obtain multi-scale features. Experimental results on the SSDD and SAR-ship-dataset show that MFFC-SDN significantly enhances SAR image target-detection performance, effectively addressing detection challenges related to large target size variations and complex environmental conditions. Compared with existing algorithms, MFFC-SDN achieves the highest A P 50 .

A multi-stage sub-structural damage localization approach using multi-label radial basis function neural network and auto-regressive model parameters

This paper proposes a Structural Health Monitoring (SHM) approach to detect localized damage by employing a Multi-Label Radial Basis Function Neural Network (ML-RBFNN). The proposed research methodology aims to identify structural damage more efficiently. This involves dividing the main structure into smaller sub-structures known as zones instead of employing a global search for damage across the entire structure. Each zone is further divided into smaller groups, including sub-zones, nodes, or elements. To reduce the complexities associated with feature extraction, auto-regressive (AR) model parameters extracted from raw time series are considered damage-sensitive features. Considering the simultaneous effects of multiple sensors can increase the number of inputs to the networks, making it challenging to reduce the complexity of the dataset. To address this problem, the principal component analysis (PCA) method is utilized to decrease the dimension of feature space. The effectiveness of the proposed approach is evaluated through a comprehensive comparative analysis against conventional single-stage damage detection methods, with a focus on computational costs and accuracy. Furthermore, the noise-sensitivity characteristics of the proposed method are explored, providing insights into their robustness under various conditions. The validation of the approach is carried out through numerical modeling using the ASCE benchmark structure and experimental data collected from the Qatar University Grandstand Simulator. The application of ML-RBFNN with AR parameters as features yielded a remarkable accuracy exceeding 98% in diagnosing damage occurrence and location for both multi-stage and single-stage SHM methods. Moreover, our proposed approach demonstrates enhanced computational efficiency compared to the single-stage method.

Quasi-single-stage optimization for permanent magnet stellarators

Advanced stellarators are typically optimized in two stages. The plasma equilibrium is optimized first, followed by the design of coils/permanent magnets. However, the coils/permanent magnets in the second stage may become too complex to achieve the desired equilibrium. To address this problem, a quasi-single-stage optimization method has been proposed. In this paper, we introduce this method for designing permanent magnet (PM) stellarators. The new approach combines straightforward PM metrics to penalize the maximum required PM thickness and the mismatch between the fixed-boundary equilibrium and the free-boundary one, along with typical physical targets. Since the degrees of freedom of the PMs are not included and directly used to minimize the objective function in this method, so we call it ‘quasi-single-stage’ optimization. We apply this quasi-single-stage optimization method to find a new quasi-axisymmetric PM design. The new design starts from MUSE, which was initially designed using a two-stage optimization approach. The resulting design, MUSE++, exhibits an order of magnitude lower quasi-symmetric error and a one-order reduction in normal field error. We show that MUSE++ has approximately 30% fewer magnets compared to a proxy model ‘MUSE-0’ that uses the same FAMUS optimization without the benefit of a single-stage equilibrium optimization. These results demonstrate that the new single-stage optimization method can concurrently improve plasma properties and simplify permanent magnet complexity.

Improving the complex therapy of dysphonia in respiratory infections and SARS-COV-2

Significantly aggravating the clinical course and negatively affecting the quality of life of patients, voice disorders in acute respiratory viral infection (ARVI) and SARS-CoV-2 require correction of treatment. Aim. To determine the effectiveness of Homeovox® in the complex therapy of dysfunctions in patients with respiratory infections and SARS-CoV-2 Materials and methods. The clinical study was conducted by a single-stage observational method, without intervention, at the clinical base of the Department of Otorhinolaryngology of the South Ural State Medical University in the period 2022–2023. The results of a study of 45 patients who suffered from acute respiratory and coronavirus infection SARS-CoV-2, accompanied by dysphonia, are presented. Results. There was a significant difference in the results of conservative treatment of patients previously divided into 2 groups according to the study design. Thus, in the second group (n=22), in addition to medical and economic standards, who received a multicomponent homeopathic medicine in accordance with the instructions, earlier recovery periods and statistically reliable restoration of vocal function were established until complete restoration of vocal function. Standard medical and economical therapy of patients of the first (n=23) and second groups included: administration of antiviral and antihistamines, local decongestants, irrigation therapy, symptomatic treatment. Conclusion. The assessment of dysphonia on three scales: the VASH scale, the Wilson Yanagihara scale and the GRBAS classification of voice disorders confirmed the restoration of voice function and recovery at an earlier date in patients who additionally took a homeopathic drug

An Instance Segmentation Method for Insulator Defects Based on an Attention Mechanism and Feature Fusion Network

Among the existing insulator defect detection methods, the automatic detection of inspection robots based on the instance segmentation algorithm is relatively more efficient, but the problem of the limited accuracy of the segmentation algorithm is still a bottleneck for increasing inspection efficiency. Therefore, we propose a single-stage insulator instance defect segmentation method based on both an attention mechanism and improved feature fusion network. YOLACT is selected as the basic instance segmentation model. Firstly, to improve the segmentation speed, MobileNetV2 embedded with an scSE attention mechanism is introduced as the backbone network. Secondly, a new feature map that combines semantic and positional information is obtained by improving the FPN module and fusing the feature maps of each layer, during which, an attention mechanism is introduced to further improve the quality of the feature map. Thirdly, in view of the problems that affect the insulator segmentation, a Restrained-IoU (RIoU) bounding box loss function which covers the area deviation, center deviation, and shape deviation is designed for object detection. Finally, for the validity evaluation of the proposed method, experiments are performed on the insulator defect data set. It is shown in the results that the improved algorithm achieves a mask accuracy improvement of 5.82% and a detection speed of 37.4 FPS, which better complete the instance segmentation of insulator defect images.

Self-Supervised Self-Organizing Clustering Network: A Novel Unsupervised Representation Learning Method.

Deep learning-based clustering methods usually regard feature extraction and feature clustering as two independent steps. In this way, the features of all images need to be extracted before feature clustering, which consumes a lot of calculation. Inspired by the self-organizing map network, a self-supervised self-organizing clustering network ( [Formula: see text]OCNet) is proposed to jointly learn feature extraction and feature clustering, thus realizing a single-stage clustering method. In order to achieve joint learning, we propose a self-organizing clustering header (SOCH), which takes the weight of the self-organizing layer as the cluster centers, and the output of the self-organizing layer as the similarities between the feature and the cluster centers. In order to optimize our network, we first convert the similarities into probabilities which represents a soft cluster assignment, and then we obtain a target for self-supervised learning by transforming the soft cluster assignment into a hard cluster assignment, and finally we jointly optimize backbone and SOCH. By setting different feature dimensions, a Multilayer SOCHs strategy is further proposed by cascading SOCHs. This strategy achieves clustering features in multiple clustering spaces. [Formula: see text]OCNet is evaluated on widely used image classification benchmarks such as Canadian Institute For Advanced Research (CIFAR)-10, CIFAR-100, Self-Taught Learning (STL)-10, and Tiny ImageNet. Experimental results show that our method significant improvement over other related methods. The visualization of features and images shows that our method can achieve good clustering results.

Http://ejournal.ukm.my/ebangi/article/view/66930/14895

Youth exposure to financial literacy information has become common in today's digital age, providing students with early financial management skills for a lifetime of well-being. However, easy access to technology and information doesn't always translate into financial literacy skills for today's students. This study aims to comprehensively understand financial literacy among undergraduates at Universiti Sains Malaysia, describing gender and ethnic group differences. It applied the theory of embeddedness to explain financial literacy variations across family backgrounds, friend networks, gender, and ethnicity. A single-stage non-probability sampling method was used. Employing purposive and quota sampling, ten informants from diverse backgrounds were interviewed. Grounded theory with open coding, axial coding, and selective coding was employed for data analysis. The findings show that undergraduate financial literacy is primarily shaped by family and social networks, where young individuals continuously develop and enhance their financial knowledge. The findings implied that educators and policymakers can customize strategies for young individuals, aligning financial literacy promotion with real-life experiences of young people which can contribute to long-term solutions for reducing youth bankruptcy.

Location-Aware Encoding for Lesion Detection in 68Ga-DOTATATE Positron Emission Tomography Images.

Lesion detection with positron emission tomography (PET) imaging is critical for tumor staging, treatment planning, and advancing novel therapies to improve patient outcomes, especially for neuroendocrine tumors (NETs). Current lesion detection methods often require manual cropping of regions/volumes of interest (ROIs/VOIs) a priori, or rely on multi-stage, cascaded models, or use multi-modality imaging to detect lesions in PET images. This leads to significant inefficiency, high variability and/or potential accumulative errors in lesion quantification. To tackle this issue, we propose a novel single-stage lesion detection method using only PET images. We design and incorporate a new, plug-and-play codebook learning module into a U-Net-like neural network and promote lesion location-specific feature learning at multiple scales. We explicitly regularize the codebook learning with direct supervision at the network's multi-level hidden layers and enforce the network to learn multi-scale discriminative features with respect to predicting lesion positions. The network automatically combines the predictions from the codebook learning module and other layers via a learnable fusion layer. We evaluate the proposed method on a real-world clinical 68Ga-DOTATATE PET image dataset, and our method produces significantly better lesion detection performance than recent state-of-the-art approaches. We present a novel deep learning method for single-stage lesion detection in PET imaging data, with no ROI/VOI cropping in advance, no multi-stage modeling and no multi-modality data. This study provides a new perspective for effective and efficient lesion identification in PET, potentially accelerating novel therapeutic regimen development for NETs and ultimately improving patient outcomes including survival.

A single-stage purification method for the precise determination of zirconium isotopic composition in geological samples by double spike MC-ICP-MS

A single-stage purification method for the precise determination of zirconium isotopic composition in geological samples by double spike MC-ICP-MS

Acute Acetabular Protrusion: the Hardinge Approach for Implant Removal and One-stage Hip Reconstruction

Acute penetration of a total hip arthroplasty into the pelvic cavity is a grave and potentially catastrophic scenario. Fortunately, this complication is uncommon and rarely encountered during a surgical career. Currently, a two-stage procedure is favoured by most surgeons, but the evidence for this is unconvincing and may expose the patient to unnecessary risks. Furthermore, a two-stage approach may be more suitable for the more common chronic migration of a loose acetabular shell, which fundamentally differs from acute pelvic penetration. We present the case of a 76-year-old man referred to our institution for reconstructive surgery following acute pelvic penetration of the acetabular shell during total hip arthroplasty. We used a single-stage Hardinge approach to retrieve the shell and successfully reconstruct the acetabulum. Specific indications for using this method are proposed. In carefully selected cases of intrapelvic implants, a single-stage method can improve patient outcomes while minimising unnecessary risks associated with the conventional two-stage approach.

AOGC: Anchor-Free Oriented Object Detection Based on Gaussian Centerness

Oriented object detection is a challenging task in scene text detection and remote sensing image analysis, and it has attracted extensive attention due to the development of deep learning in recent years. Currently, mainstream oriented object detectors are anchor-based methods. These methods increase the computational load of the network and cause a large amount of anchor box redundancy. In order to address this issue, we proposed an anchor-free oriented object detection method based on Gaussian centerness (AOGC), which is a single-stage anchor-free detection method. Our method uses contextual attention FPN (CAFPN) to obtain the contextual information of the target. Then, we designed a label assignment method for the oriented objects, which can select positive samples with higher quality and is suitable for large aspect ratio targets. Finally, we developed a Gaussian kernel-based centerness branch that can effectively determine the significance of different anchors. AOGC achieved a mAP of 74.30% on the DOTA-1.0 datasets and 89.80% on the HRSC2016 datasets, respectively. Our experimental results show that AOGC exhibits superior performance to other methods in single-stage oriented object detection and achieves similar performance to the two-stage methods.

A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention

Geo-localization has been widely applied as an important technique to get the longitude and latitude for unmanned aerial vehicle (UAV) navigation in outdoor flight. Due to the possible interference and blocking of GPS signals, the method based on image retrieval, which is less likely to be interfered with, has received extensive attention in recent years. The geo-localization of UAVs and satellites can be achieved by querying pre-obtained satellite images with GPS-tagged and drone images from different perspectives. In this paper, an image transformation technique is used to extract cross-view geo-localization information from UAVs and satellites. A single-stage training method in UAV and satellite geo-localization is first proposed, which simultaneously realizes cross-view feature extraction and image retrieval, and achieves higher accuracy than existing multi-stage training techniques. A novel piecewise soft-margin triplet loss function is designed to avoid model parameters being trapped in suboptimal sets caused by the lack of constraint on positive and negative samples. The results illustrate that the proposed loss function enhances image retrieval accuracy and realizes a better convergence. Moreover, a data augmentation method for satellite images is proposed to overcome the disproportionate numbers of image samples. On the benchmark University-1652, the proposed method achieves the state-of-the-art result with a 6.67% improvement in recall rate (R@1) and 6.13% in average precision (AP). All codes will be publicized to promote reproducibility.

End-PolarT: Polar Representation for End-to-End Scene Text Detection

Deep learning has achieved great success in text detection, where recent methods adopt inspirations from segmentation to detect scene texts. However, most segmentation based methods have high computation cost in pixel-level classification and post refinements. Moreover, they still faces challenges like arbitrary directions, curved texts, illumination and so on. Aim to improve detection accuracy and computation cost, we propose an end-to-end and single-stage method named as End-PolarT network by generating contour points in polar coordinates for text detection. End-PolarT not only regress locations of contour points instead of pixels to relieve high computation cost, but also fits with intrinsic characteristics of text instances by centers and contours to suppress mislabeling boundary pixels. To cope with polar representation, we further propose polar IoU and centerness as key parts of loss functions to generate effective paradigms for text detection. Compared with the existing methods, End-PolarT achieves superior results by testing on several public datasets, thus keeping balance between efficiency and effectiveness in complicated scenes.

Two Scenario-Based Heuristics for Stochastic Shift Design Problem with Task-Based Demand

In this paper, we propose a deterministic shift design model with task-based demand and give the corresponding stochastic version with a probability constraint such that the shift plan designed is staffed with the workforce with a certain probability of performing all given tasks. Since we currently find no suitable methods for solving this stochastic model from the literature related to solving stochastic shift design models, we developed a single-stage heuristic method based on statistics, whose main idea is to reduce the occurrence of manpower shortage by prolonging the resource occupation time of a task, but this leads to a serious waste of resources, which is common in solving resource allocation problems with uncertain durations. To reduce the cost of wastage, we also propose a two-stage heuristic approach that is a two-stage heuristic with an evolutionary strategy. The two heuristics show their effectiveness in solving the proposed stochastic model in numerical experiments, and the two-stage heuristic significantly outperforms the one-stage heuristic in cost optimization and solution time stability.

A stochastic investment decision making method for distribution system resilience enhancement considering automation, hardening and distributed energy resources

Extreme weather events have always posed as a serious threat to the power system. In recent years the frequent occurrences of extreme events highlight the requirement for a resilient distribution system capable of maximizing load restoration under the most adverse situations. Because the requirement to reinforce the distribution system appears to be an expensive approach, it is crucial to make the proper investment decisions. In this regard, this article introduces a single-stage stochastic multi-criteria optimization method for maximizing network resilience over a series of investments while minimizing overall costs. This method conducts different categories of investments including the installation of sectionalizing switches, distributed energy resources, and hardening of overhead distribution lines. The optimization method is based on a genetic algorithm and can be used to address a large-scale network. Several numerical studies are tested on the IEEE 69-bus system to verify the effectiveness of the proposed method. The results of the investment decision-making method show the significance of combining planning and operational preparedness in distribution systems to respond to extreme weather events.