Search

EU's Carbon Border Adjustment Mechanism (CBAM) has been promulgated. The steel industry is included in CBAM, which will undoubtedly affect the export trade of China's steel manufacturers to the EU. It is necessary to analyse the impact of CBAM on China's steel industry quantitatively. By establishing a system dynamics model that can simulate the operation mechanism of the steel supply chain in China, we have clarified the impacts of CBAM on the China's steel export and carbon reduction. Moreover, we have reasonably set up scenarios that represent the possible decarbonisation paths of China's steel industry and evaluated the implementation effect of each policy in these scenarios. The analysis shows that CBAM will lead to a decrease of 32% in export volume and a loss of 58% in export profits for China's steel exporters. The environmental benefits of CBAM are limited. Due to the low trade exposure, CBAM is unable to promote a large-scale carbon reduction in the China's steel industry. Although CBAM will bring great pressure to steel exporters, a comprehensive policy of adopting more emission reduction measures can greatly improve the carbon reduction efficiency, effectively reduce the negative impact of CBAM, and maintain the stability of steel exports. [ABSTRACT FROM AUTHOR]

As a key step of gesture recognition, gesture segmentation can effectively reduce the impact of complex backgrounds on recognition results and improve the accuracy of gesture recognition. The gesture segmentation algorithm based on image processing is easily affected by the complex backgrounds in the image, resulting in poor gesture segmentation effect and low precision. To improve the effect of gesture segmentation under complex backgrounds and increase the accuracy of gesture recognition systems, this paper proposes a gesture segmentation method based on FCN combined with CBAM-ResNet50 network model. The trunk network of FCN is proposed as a new ResNet-50 framework. CBAM attention mechanism is introduced into the residual structure to enhance the extraction ability of multi-scale context information. And we achieve deep feature and shallow feature extraction and fusion by combining expansion convolution and enhancing the parameters of the convolutional layer, so as to improve the precision of gesture segmentation. In addition, the methods of data preprocessing and loading pre-training weights are used to enhance the model's robustness and performance and expedite the training time. In the experiments, the NUS-II gesture data set with a complex background was used for testing. The average recognition rate of gesture pixels was 97.41% and gesture IoU was 94.54%. The experimental results show that the proposed method has a good effect on gesture segmentation for gesture images with complex backgrounds. [ABSTRACT FROM AUTHOR]

Strawberry crops are susceptible to a wide range of pests and diseases, some of which are insidious and diverse due to the shortness of strawberry plants, and they pose significant challenges to accurate detection. Although deep learning-based techniques to detect crop pests and diseases are effective in addressing these challenges, determining how to find the optimal balance between accuracy, speed, and computation remains a key issue for real-time detection. In this paper, we propose a series of improved algorithms based on the YOLOv8 model for strawberry disease detection. These include improvements to the Convolutional Block Attention Module (CBAM), Super-Lightweight Dynamic Upsampling Operator (DySample), and Omni-Dimensional Dynamic Convolution (ODConv). In experiments, the accuracy of these methods reached 97.519%, 98.028%, and 95.363%, respectively, and the F1 evaluation values reached 96.852%, 97.086%, and 95.181%, demonstrating significant improvement compared to the original YOLOv8 model. Among the three improvements, the improved model based on CBAM has the best performance in training stability and convergence, and the change in each index is relatively smooth. The model is accelerated by TensorRT, which achieves fast inference through highly optimized GPU computation, improving the real-time identification of strawberry diseases. The model has been deployed in the cloud, and the developed client can be accessed by calling the API. The feasibility and effectiveness of the system have been verified, providing an important reference for the intelligent research and application of strawberry disease identification. [ABSTRACT FROM AUTHOR]

Cracks are a common type of damage found on the surfaces of concrete buildings and roads. Accurately identifying the width and direction of these cracks is critical for maintaining and evaluating such structures. However, challenges such as irregular crack shapes and complex background interference persist in the crack identification task. To address these challenges, we propose a semantic segmentation network for cracks (DSU-Net) based on U-Net. A lightweight decoder is built through depthwise separable convolution to reduce model complexity and better retain the high-level features extracted by the encoder. Three modules are designed to improve the performance of the model. First, a feature enhancement module (DCM) that combines CBAM and squeeze excitation (cSE) is constructed to further enhance and optimize the intermediate features extracted by the encoder. Secondly, a neighboring layer information fusion module (NIF) is constructed to enrich the semantic information of extracted features. Finally, a feature refinement module (FRM) is constructed using multi-layer convolutional skip connections to make the final refinement of the features extracted by the model. Experiments were conducted using three datasets: DeepCrack, Crack500, and CCSS. The segmentation effect was tested, and nine models were used for comparative experiments. The test results showed an average improvement of 1.29% and 1.89% in the three datasets compared to the suboptimal models MIoU and F1, respectively. [ABSTRACT FROM AUTHOR]

In the realm of underwater object detection, conventional methodologies often encounter challenges in accurately identifying and detecting small targets. These difficulties stem primarily from the intricate nature of underwater environments, suboptimal lighting conditions, and the diminutive scale of the targets themselves. To address this persistent challenge, the MarineYOLO network is introduced. This approach involves refining the conventional C2f module into the EC2f module, alongside the integration of the Efficient Multi-scale Attention (EMA) module into YOLOv8. Additionally, the Convolutional Block Attention Module (CBAM) is introduced to further refine the Feature Pyramid Network (FPN), facilitating enhanced feature extraction pertinent to small targets. Furthermore, the conventional CIoU is replaced with Wise-IoU to augment the precision and stability of target localization. Experimental findings demonstrate that MarineYOLO achieves an average precision (AP) of 78.5% on the RUOD dataset and 88.1% on the URPC dataset, marking improvements of 12.2% and 16.8%, respectively, compared to YOLOv8n. As an emerging paradigm in underwater object detection, MarineYOLO harbors significant potential in both practical applications and scholarly endeavors, furnishing an efficacious remedy to the challenges associated with detecting small targets in underwater settings. [ABSTRACT FROM AUTHOR]

With the frequent occurrence of natural disasters and the acceleration of urbanization, it is necessary to carry out efficient evacuation, especially when earthquakes, fires, terrorist attacks, and other serious threats occur. However, due to factors such as small targets, complex posture, occlusion, and dense distribution, the current mainstream algorithms still have problems such as low precision and poor real-time performance in crowd person detection. Therefore, this paper proposes EAAnet, a crowd person detection algorithm. It is based on YOLOv5, with CBAM (Convolutional Block Attention Module) introduced into the backbone, BiFPN (Bidirectional Feature Pyramid Network) introduced into the neck, and combined with a loss function of CIoU_Loss to better predict the person number. The experimental results show that compared with other mainstream detection algorithms, EAAnet has achieved significant improvement in precision and real-time performance. The precision value of all categories was 78.6%, which was increased by 1.8. Among these, the categories of riders and partially visible person were increased by 4.6 and 0.8, respectively. At the same time, the parameter number of EAAnet is only 7.1M, with a calculation amount of 16.0G FLOPs. Therefore, it is proved that EAAnet has the ability of the efficient real-time detection of the crowd person and is feasible in the field of emergency management. [ABSTRACT FROM AUTHOR]

Bird research contributes to understanding species diversity, ecosystem functions, and the maintenance of biodiversity. By analyzing bird images and the audio of birds, we can monitor bird distribution, abundance, and behavior to better understand the health of ecosystems. However, bird images and audio involve a vast amount of data. To improve the efficiency of data transmission and storage efficiency and save bandwidth, compressive sensing can overcome this challenge. Compressive sensing is a technique that uses the sparsity of signals to recover original data from a small number of linear measurements. This paper introduces a deep neural network based on the Iterative Shrinkage Thresholding Algorithm (ISTA) and a Convolutional Block Attention Module (CBAM), CBAM_ISTA-Net+, for the compressive reconstruction of bird images, audio Mel spectrograms and wavelet transform spectrograms. Using 45 bird species as research subjects, including 20 bird images, 15 audio-generated Mel spectrograms, and 10 audio wavelet transform (WT) spectrograms, the experimental results show that CBAM_ISTA-Net+ achieves a higher peak signal-to-noise ratio (PSNR) at different compression ratios. At a compression ratio of 50%, the average PSNR of the three datasets reaches 33.62 dB, 55.76 dB, and 38.59 dB, while both the Mel spectrogram and wavelet transform spectrogram achieve more than 30 dB at compression ratios of 25–50%. These results highlight the effectiveness of CBAM_ISTA-Net+ in maintaining high reconstruction quality even under significant compression, demonstrating its potential as a valuable tool for efficient data management in ecological research. [ABSTRACT FROM AUTHOR]

Psychological research shows that the tidiness of fresh display in supermarkets will affect consumers'desire to buy to a great extent. Therefore, whether the supermarket administrator can organize the shelves in time and keep the fresh on the shelves neatly placed has a great impact on their sales performance. In order to solve this problem, we use the method of calculating the distance between the neatly placed pictures and the texture feature vector of the collected test images to measure the similarity of the two pictures, so as to determine whether the goods are neatly placed. HistNet is a texture classification network which takes ResNet as the baseline network and introduces histogram layer, which can classify images with texture features very well. Based on HistNet, we use large margin cosine loss(LMC loss) and CrossEntropy loss for joint training to improve the discrimination ability of the network by increasing inter-class differences and reducing intra-class differences. Then change the convolution method of learning bin center in the histogram, make the texture features learned by the histogram more abundant, and add convolutional block attention module(CBAM) attention before the first convolution in the histogram layer to further improve the network performance. On the basis of the optimized network, we remove the last layer and measure the extracted texture features directly. We do a large number of experiments on a variety of distance measurement methods and choose cosine distance to judge the tidiness of fresh display in the supermarket. We tested the improved algorithm on DTD, MINC-2500 and GTOS-mobile datasets, and the accuracy reached 71.79%, 83.21% and 81.24%, respectively. The optimized network improves the accuracy of texture classification. In the application of supermarket fresh tidiness detection, it can well distinguish between tidiness and confusion, and has a broad application prospect. [ABSTRACT FROM AUTHOR]

As one of the early manifestations of road pavement structure degradation, road cracks will accelerate the deterioration of the road if not detected and repaired in time. Aiming at the problems of low recall and incomplete crack detection in current road detection, based on the U-Net network, this paper proposed an Attention-Dynamic Snake Convolution U-Net (ADSC-U-Net) network. Firstly, the dynamic snake-shaped convolution was added to the normal downsampling process to make the network adaptively focus on the slender and curved local features, which can solve the problem of low accuracy of small crack detection. Secondly, the attention mechanism was used to pay better attention to the significant features of positive samples under the condition of a large proportion gap between positive and negative samples, which solved the problem of the poor crack integrity detection effect. Finally, the dataset was expanded by random vertical and horizontal flip operations, which solved the problem of network training overfitting caused by the small-scale datasets. The experimental results showed that, when the input image had a resolution of 480 × 320, evaluation indices P, R, and F1 of ADSC-U-Net on the self-built dataset were 74.44%, 68.77%, and 69.42%, respectively. Compared to SegNet, DeepLab, and DeepCrack, the P was improved by 1.90%, 2.49%, and 11.64%, respectively; the R was improved by 8.01%, 4.70%, and 59.58%, respectively; and the comprehensive evaluation index F1 was improved by 5.73%, 4.02%, and 55.87%, respectively, which proves the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have made substantial contributions to the domain of plant disease diagnosis, attaining noteworthy levels of accuracy. The primary objective of this study is to enhance the capabilities of CNNs within this particular field. In this article, we suggest a unique strategy utilizing MultiRes blocks and attention mechanisms to enhance the classification performance of CNNs for plant diseases. Our solution involves stacking four MultiRes blocks, each of which gradually increases the number of filters to prevent excessive propagation of memory requirements to deeper network nodes. In order to collect more spatial information, we also implement a residual link and a 1 × 1 convolutional layer. After each MultiRes block, we employ a Convolutional Block Attention Module to emphasize vital information and decrease redundant noise by inferring attention mappings along the channel and spatial dimensions. Finally, our method includes the use of a Dilated Spatial Pyramid Pooling module which is designed to extract information from the input image at various scales. Our evaluation leveraged a comprehensive dataset compiled from multiple sources, including the New Plant Diseases Dataset, Corn Dataset, and Coffee Dataset, encompassing a wide range of plant leaves both healthy and diseased, across various classes. This diverse collection of images, augmented by several techniques to enhance data variance and model robustness, provided a solid foundation for assessing the performance of our proposed model. Experiments demonstrate that this method outperforms conventional machine learning techniques and delivers high classification accuracy for plant diseases, with a precision of 99.34%, recall of 99.25%, f1-score of 99.29%, and accuracy of 99.35%. Our proposed model has the potential to considerably enhance the effectiveness and efficiency of diagnosing plant diseases in the agricultural industry. [ABSTRACT FROM AUTHOR]

Brain tumors are diseases characterized by abnormal cell growth within or around brain tissues, including various types such as benign and malignant tumors. However, there is currently a lack of early detection and precise localization of brain tumors in MRI images, posing challenges to diagnosis and treatment. In this context, achieving accurate target detection of brain tumors in MRI images becomes particularly important as it can improve the timeliness of diagnosis and the effectiveness of treatment. To address this challenge, we propose a novel approach-the YOLO-NeuroBoost model. This model combines the improved YOLOv8 algorithm with several innovative techniques, including dynamic convolution KernelWarehouse, attention mechanism CBAM (Convolutional Block Attention Module), and Inner-GIoU loss function. Our experimental results demonstrate that our method achieves mAP scores of 99.48 and 97.71 on the Br35H dataset and the open-source Roboflow dataset, respectively, indicating the high accuracy and efficiency of this method in detecting brain tumors inMRI images. This research holds significant importance for improving early diagnosis and treatment of brain tumors and provides new possibilities for the development of the medical image analysis field. [ABSTRACT FROM AUTHOR]

Medical image segmentation through the use of deep learning is becoming a trend targeting to automate disease detection and provide effective treatment. Traditionally huge manual efforts are associated with medical image processing by medical staff. The wide use of neural networks driven by the progressive advancement in computing power and the availability of training data provides instrumental means to automate medical image processing. A polyp refers to an abnormal growth that can occur in various parts of the body. While the majority of polyps are noncancerous or benign, there are instances where certain types can develop into cancer. Detecting and segmenting polyps is highly valuable for identifying early signs and potentially enabling more effective treatment of colon cancer. In this paper, we introduce a novel method for segmenting polyp images. The proposed method utilizes convolutional neural networks (CNNs) and employs an enhanced attention mechanism. To cater to a more granular attention view, this paper enhances the Convolutional Block Attention Module by introducing the multi‐focal channel attention (MFCA) concept, which we call MFCA. In the proposed MFCA channel attention, focal attention spots allow us to consider scattered areas of interest more effectively. To evaluate the effectiveness of the proposed method, multiple experiments are conducted on five well‐known benchmark datasets for polyp image segmentation: Kvasir, CVC‐Clinic DB, CVC‐Colon DB, CVC‐T, and ETIS‐Larib. Various testing scenarios are examined, including the impact of two focal attention spots and four focal attention spots. The experimental results demonstrated that the proposed method achieved superior performance compared to previous state‐of‐the‐art techniques on two of the benchmark datasets, and ranked second on a third dataset. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence technology with medicine has rapidly evolved, with increasing demands for quality of life. However, falls remain a significant risk leading to severe injuries and fatalities, especially among the elderly. Therefore, the development and application of computer vision-based fall detection technologies have become increasingly important. In this paper, firstly, the keypoint detection algorithm ViTPose++ is used to obtain the coordinates of human body keypoints from the camera images. Human skeletal feature maps are generated from this keypoint coordinate information. Meanwhile, human dense feature maps are produced based on the DensePose algorithm. Then, these two types of feature maps are confused as dual-channel inputs for the model. The convolutional gated recurrent unit is introduced to extract the frame-to-frame relevance in the process of falling. To further integrate features across three dimensions (spatio-temporal-channel), a dual-channel fall detection algorithm based on video streams is proposed by combining the Convolutional Block Attention Module (CBAM) with the ConvGRU. Finally, experiments on the public UR Fall Detection Dataset demonstrate that the improved ConvGRU-CBAM achieves an F1 score of 92.86% and an AUC of 95.34%. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Facial emotion recognition has become a complicated task due to individual variations in facial characteristics, as well as racial and cultural variances. Different psychological studies show that there are complex expressions other than basic emotions which are made up of two basic emotions like"Happily Disgusted", "Happily Surprised", "Sadly Surprised", etc. Compound emotion recognition is challenging due to very less publicly available compound emotion datasets which are imbalanced too. In this paper, we have proposed an LSwin-CBAM for the classification of compound emotions. To address the problem of the imbalanced dataset, the proposed model exploits the cutmix augmentation technique for data augmentation. It also incorporates the CBAM attention mechanism to emphasize the relevant features in an image and swin transformer with fewer swin transformer blocks which leads to less computational complexity in terms of trainable parameters and improves the overall classification accuracy as well. The experimental results of LSwin-CBAM on RAF-DB and EmotioNet datasets show that the proposed transformer-based network can well recognize compound emotions. [ABSTRACT FROM AUTHOR]

In the contemporary context, pest detection is progressively moving toward automation and intelligence. However, current pest detection algorithms still face challenges, such as lower accuracy and slower operation speed in detecting small objects. To address this issue, this study presents a crop pest target detection algorithm, GLU-YOLOv8, designed for complex scenes based on an enhanced version of You Only Look Once version 8 (YOLOv8). The algorithm introduces the SCYLLA-IOU (SIOU) loss function, which enhances the model generalization to various pest sizes and shapes by ensuring smoothness and reducing oscillations during training. Additionally, the algorithm incorporates the Convolutional Block Attention Module (CBAM) and Locality Sensitive Kernel (LSK) attention mechanisms to boost the pest target features. A novel Gated Linear Unit CONV (GLU-CONV) is also introduced to enhance the model's perceptual and generalization capabilities while maintaining performance. Furthermore, GLU-YOLOv8 includes a small-object detection layer with a feature map size of 160 × 160 to extract more features of small-target pests, thereby improving detection accuracy and enabling more precise localization and identification of small-target pests. The study conducted a comparative analysis between the GLU-YOLOv8 model and other models, such as YOLOv8, Faster RCNN, and RetinaNet, to evaluate detection accuracy and precision. In the Scolytidae forestry pest dataset, GLU-YOLOv8 demonstrated an improvement of 8.2% in mAP@0.50 for small-target detection compared to the YOLOv8 model, with a resulting mAP@0.50 score of 97.4%. Specifically, on the IP102 dataset, GLU-YOLOv8 outperforms the YOLOv8 model with a 7.1% increase in mAP@0.50 and a 5% increase in mAP@0.50:0.95, reaching 58.7% for mAP@0.50. These findings highlight the significant enhancement in the accuracy and recognition rate of small-target detection achieved by GLU-YOLOv8, along with its efficient operational performance. This research provides valuable insights for optimizing small-target detection models for various pests and diseases. [ABSTRACT FROM AUTHOR]

A cross-sectional survey was conducted to explore university teachers' stages of concerns (SoC) about the adoption of the Moodle LMS at the Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development (AAMUSTED), Ghana. Hundred and thirty-one (131) participants (university teachers) were randomly selected from the Kumasi Campus of AAMUSTED to participate in the study. Results revealed that the university teachers had primary and secondary concerns at the personal and consequence stages of the SoC, respectively. At the awareness stage, they reported having low levels of concerns. Participants' socio-demographics (i.e. gender, age, educational qualification, rank, and teaching experience) had no significant relationship with the overall stages of concerns. Implications of these findings have been discussed to help address university teachers' concerns about the adoption of online learning management systems in HEIs in low- and middle-income countries, where online education is rife with issues. [ABSTRACT FROM AUTHOR]

In the realm of underwater object detection, conventional methodologies often encounter challenges in accurately identifying and detecting small targets. These difficulties stem primarily from the intricate nature of underwater environments, suboptimal lighting conditions, and the diminutive scale of the targets themselves. To address this persistent challenge, the MarineYOLO network is introduced. This approach involves refining the conventional C2f module into the EC2f module, alongside the integration of the Efficient Multi-scale Attention (EMA) module into YOLOv8. Additionally, the Convolutional Block Attention Module (CBAM) is introduced to further refine the Feature Pyramid Network (FPN), facilitating enhanced feature extraction pertinent to small targets. Furthermore, the conventional CIoU is replaced with Wise-IoU to augment the precision and stability of target localization. Experimental findings demonstrate that MarineYOLO achieves an average precision (AP) of 78.5% on the RUOD dataset and 88.1% on the URPC dataset, marking improvements of 12.2% and 16.8%, respectively, compared to YOLOv8n. As an emerging paradigm in underwater object detection, MarineYOLO harbors significant potential in both practical applications and scholarly endeavors, furnishing an efficacious remedy to the challenges associated with detecting small targets in underwater settings.

This study clarifies the impact of the carbon border adjustment mechanism (CBAM) on the export activities of enterprises that produce six main affected industrial products: iron and steel, cement, fertilizer, aluminium, electricity, and hydrogen. Secondary data is included in descriptive statistics, thereby analyzing the context, current situation and economic-technical impact on the export activities of each item, especially the 05 items that Vietnam exports to the EU market. From there, the article proposes recommendations and strategies to respond to the opportunities and challenges brought by CBAM. Research results show that CBAM puts pressure on export businesses at an early stage. However, in the long term, this policy will help businesses increase competitiveness and create surplus value with reduced costs.

The assessment of rolling bearing reliability is vital for ensuring mechanical operational safety and minimizing maintenance costs. Due to the difficulty in obtaining data on the performance degradation and failure time of rolling bearings, traditional methods for reliability assessment are challenged. This paper introduces a novel hybrid method for the reliability assessment of rolling bearings, combining the convolutional neural network (CNN)‐convolutional block attention module (CBAM)‐ bidirectional long short‐term memory (BiLSTM) network with the Wiener process. The approach comprises three distinct stages: Initially, it involves acquiring two‐dimensional time‐frequency representations of bearings at various operational phases using Continuous Wavelet Transform. Subsequently, the CNN‐CBAM‐BiLSTM network is employed to establish health index (HI) for the bearings and to facilitate the extraction of deep features, serving as input for the Wiener process. The final stage applies the Wiener process to evaluate the bearings' reliability, characterizing the HI and quantifying uncertainties. The experiment is performed on bearing degradation data and the results indicate the effectiveness and superiority of the proposed hybrid method. [ABSTRACT FROM AUTHOR]

Ship detection from visual image (SDVI) plays a significant role in terminal management, cross-border ship detection and marine target tracking. Compared to synthetic aperture radar (SDSAR) based ship detection, SDVI has superior performance on accuracy and speed. WSA-YOLOv5s, a new algorithm with a Window Self-Attention (WSA) module is proposed in this paper to substantially improve the ability of detecting small targets while enhancing the ability of large target recognition marginally. The following fine tunings are made based on the original YOLOv5s network. (1) Perform target data enhancement for the dataset’s imbalanced classification by using the improved mix-up algorithm and focal loss; (2) Replace CSP structure in the original backbone with the WSA module, and add one more interaction layer than the original to increase the exchange of high-level semantic information; (3) Add CBAM module to reduce the influence of background interference factors by using spatial attention and channel attention. Experiments are carried out on the Singapore Maritime Dataset (SMD). The results show that our model is greatly improved in all aspects. And the mAP increases from 73 to 90.5%, while keeping the number of model parameters small. [ABSTRACT FROM AUTHOR]

A cross-sectional survey was conducted to explore university teachers’ stages of concerns (SoC) about the adoption of the Moodle LMS at the Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development (AAMUSTED), Ghana. Hundred and thirty-one (131) participants (university teachers) were randomly selected from the Kumasi Campus of AAMUSTED to participate in the study. Results revealed that the university teachers had primary and secondary concerns at the personal and consequence stages of the SoC, respectively. At the awareness stage, they reported having low levels of concerns. Participants’ socio-demographics (i.e. gender, age, educational qualification, rank, and teaching experience) had no significant relationship with the overall stages of concerns. Implications of these findings have been discussed to help address university teachers’ concerns about the adoption of online learning management systems in HEIs in low- and middle-income countries, where online education is rife with issues.

Abstract While deep learning technologies have made remarkable progress in generating deepfakes, their misuse has become a well-known concern. As a result, the ubiquitous usage of deepfakes for increasing false information poses significant risks to the security and privacy of individuals. The primary objective of audio spoofing detection is to identify audio generated through numerous AI-based techniques. Several techniques for fake audio detection already exist using machine learning algorithms. However, they lack generalization and may not identify all types of AI-synthesized audios such as replay attacks, voice conversion, and text-to-speech (TTS). In this paper, a deep layered model, i.e., VGGish, along with an attention block, namely Convolutional Block Attention Module (CBAM) for spoofing detection, is introduced. Our suggested model successfully classifies input audio into two classes: Fake and Real, converting them into mel-spectrograms, and extracting their most representative features due to the attention block. Our model is a significant technique to utilize for audio spoofing detection due to a simple layered architecture. It captures complex relationships in audio signals due to both spatial and channel features present in an attention module. To evaluate the effectiveness of our model, we have conducted in-depth testing using the ASVspoof 2019 dataset. The proposed technique achieved an EER of 0.52% for Physical Access (PA) attacks and 0.07 % for Logical Access (LA) attacks.

Abstract In carbonate reservoirs characterized by the fracture‐cavity system as storage spaces, the drilling process is highly prone to the loss of drilling fluid. This not only affects drilling efficiency but can also lead to severe accidents, such as blowouts. Therefore, it is crucial to understand the distribution pattern of these fractures. However, the formation of carbonate rock fracture‐cavity system systems, being controlled by various factors, is difficult to precisely identify. This limitation hampers the efficient development of such types of oil and gas fields. This paper presents a case study of the M55 sub‐section carbonate gas reservoir in the Sulige gasfield, proposing an improved You Only Look Once v5s (YOLOv5s) deep learning algorithm. It utilizes enhanced training with conventional logging data to identify response characteristics of fractures in the carbonate reservoirs. And its identification results have been confirmed to be accurate by various fracture data obtained through different means, such as the core samples, cast thin section photographs, imaging logging data, and seismic attributes. This method incorporates the Ghost convolution module to replace the Conv module in the backbone network of the YOLOv5s model, and modifies the C3 module into a Ghost Bottleneck module, effectively making the model more lightweight. Additionally, a Convolutional Block Attention Module is integrated into the Neck network, enhancing the model's feature extraction capabilities. Finally, the method employs the Efficient Intersection over Union Loss function instead of the Complete Intersection over Union Loss, reducing the network's regression loss. The validation results using actual data demonstrate that this method achieves an average recognition accuracy of 87.3% for the fracture‐cavity system, which is a 3% improvement over the baseline model (YOLOv5s). This enhancement is beneficial for precisely locating the drilling fluid loss positions in carbonate reservoirs.

Abstract Hot dry rock (HDR) resources are gaining increasing attention as a significant renewable resource due to their low carbon footprint and stable nature. When assessing the potential of a conventional geothermal resource, a temperature field distribution is a crucial factor. However, the available geostatistical and numerical simulations methods are often influenced by data coverage and human factors. In this study, the Convolution Block Attention Module (CBAM) and Bottleneck Architecture were integrated into UNet (CBAM-B-UNet) for simulating the geothermal temperature field. The proposed CBAM-B-UNet takes in a geological model containing parameters such as density, thermal conductivity, and specific heat capacity as input, and it simulates the temperature field by dynamically blending these multiple parameters through the neural network. The bottleneck architectures and CBAM can reduce the computational cost while ensuring accuracy in the simulation. The CBAM-B-UNet was trained using thousands of geological models with various real structures and their corresponding temperature fields. The method’s applicability was verified by employing a complex geological model of hot dry rock. In the final analysis, the simulated temperature field results are compared with the theoretical steady-state crustal ground temperature model of Gonghe Basin. The results indicated a small error between them, further validating the method's superiority. During the temperature field simulation, the thermal evolution law of a symmetrical cooling front formed by low thermal conductivity and high specific heat capacity in the center of the fault zone and on both sides of granite was revealed. The temperature gradually decreases from the center towards the edges.

【Objective】It is always difficult to timely locate the location of the network attack and achieve rapid deployment of defense strategies when the smart grid is attacked by the network.【Methods】In order to solve this problem, this article proposes a Convolutional Neural Network (CNN) model that integrates Convolutional Block Attention Modules (CBAM) (CNN-CBAM) to detect False Data Injection Attack (FDIA) positions. The attack identification problem of FDIA is modeled as a multi label classification problem, where CNN is used to extract spatial features of the data. The CBAM module can be directly integrated into the convolution operation of the CNN module, which not only focuses on important parameter information from the perspective of spatial domain, but also considers feature relationships in the channel domain, and allocates attention to the input data from two dimensions to improve the performance of the model.【Results】The performance of the proposed CNN-CBAM network FDIA position detection model is verified on Institute of Electrical and Electronics Engineers (IEEE) 14 and IEEE118 node systems. The experimental results show that the FDIA position detection rates of CNN-CBAM on IEEE14 and IEEE118 node systems are 98.25%and 96.72%, respectively.【Conclusion】Compared with other methods, the CNN-CBAM network model proposed in this paper can effectively extract the spatiotemporal characteristics between data, with improved existence of FDIA. It also im-proves the accuracy of attack location identification with better robustness.

When it comes to road environment perception, millimeter-wave radar with a camera facilitates more reliable detection than a single sensor. However, the limited utilization of radar features and insufficient extraction of important features remain pertinent issues, especially with regard to the detection of small and occluded objects. To address these concerns, we propose a camera-radar fusion with radar channel extension and a dual-CBAM-FPN (CRFRD), which incorporates a radar channel extension (RCE) module and a dual-CBAM-FPN (DCF) module into the camera-radar fusion net (CRF-Net). In the RCE module, we design an azimuth-weighted RCS parameter and extend three radar channels, which leverage the secondary redundant information to achieve richer feature representation. In the DCF module, we present the dual-CBAM-FPN, which enables the model to focus on important features by inserting CBAM at the input and the fusion process of FPN simultaneously. Comparative experiments conducted on the NuScenes dataset and real data demonstrate the superior performance of the CRFRD compared to CRF-Net, as its weighted mean average precision (wmAP) increases from 43.89% to 45.03%. Furthermore, ablation studies verify the indispensability of the RCE and DCF modules and the effectiveness of azimuth-weighted RCS. [ABSTRACT FROM AUTHOR]

In order to improve the reading efficiency of pointer meter, this paper proposes a reading method based on LinkNet. Firstly, the meter dial area is detected using YOLOv8. Subsequently, the detected images are fed into the improved LinkNet segmentation network. In this network, we replace traditional convolution with partial convolution, which reduces the number of model parameters while ensuring accuracy is not affected. Remove one pair of encoding and decoding modules to further compress the model size. In the feature fusion part of the model, the CBAM (Convolutional Block Attention Module) attention module is added and the direct summing operation is replaced by the AFF (Attention Feature Fusion) module, which enhances the feature extraction capability of the model for the segmented target. In the subsequent rotation correction section, this paper effectively addresses the issue of inaccurate prediction by CNN networks for axisymmetric images within the 0–360° range, by dividing the rotation angle prediction into classification and regression steps. It ensures that the final reading part receives the correct angle of image input, thereby improving the accuracy of the overall reading algorithm. The final experimental results indicate that our proposed reading method has a mean absolute error of 0.20 and a frame rate of 15. [ABSTRACT FROM AUTHOR]

Object detection via remote sensing encounters significant challenges due to factors such as small target sizes, uneven target distribution, and complex backgrounds. This paper introduces the K-CBST YOLO algorithm, which is designed to address these challenges. It features a novel architecture that integrates the Convolutional Block Attention Module (CBAM) and Swin-Transformer to enhance global semantic understanding of feature maps and maximize the utilization of contextual information. Such integration significantly improves the accuracy with which small targets are detected against complex backgrounds. Additionally, we propose an improved detection network that combines the improved K-Means algorithm with a smooth Non-Maximum Suppression (NMS) algorithm. This network employs an adaptive dynamic K-Means clustering algorithm to pinpoint target areas of concentration in remote sensing images that feature varied distributions and uses a smooth NMS algorithm to suppress the confidence of overlapping candidate boxes, thereby minimizing their interference with subsequent detection results. The enhanced algorithm substantially bolsters the model's robustness in handling multi-scale target distributions, preserves more potentially valid information, and diminishes the likelihood of missed detections. This study involved experiments performed on the publicly available DIOR remote sensing image dataset and the DOTA aerial image dataset. Our experimental results demonstrate that, compared with other advanced detection algorithms, K-CBST YOLO outperforms all its counterparts in handling both datasets. It achieved a 68.3% mean Average Precision (mAP) on the DIOR dataset and a 78.4% mAP on the DOTA dataset. [ABSTRACT FROM AUTHOR]

To rapidly and accurately identify blueberry fruit maturity under complex field conditions, a lightweight detection model for blueberry maturity (MSC-YOLOv8)was proposed based on YOLOv8n. YOLOv8n, a recent addition to the YOLO series, offers the fastest detection speed and the lightest architecture, albeit with slightly lower recognition accuracy. Considering these attributes, YOLOv8n was chosen as the foundational model for blueberry maturity recognition. Compared to traditional object detection algorithms, YOLOv8 boasts a significantly faster detection speed. By using a single forward propagation to obtain detection results for all targets simultaneously, YOLOv8 achieves low latency, making it suitable for realtime applications. Additionally, YOLOv8 demonstrates high detection accuracy, rivaling advanced object detection algorithms, and excels particularly in detecting small targets. These advantages make YOLOv8 a superior choice for this application. Before training the model, the original blueberry datasets need to be augmented using Deep Convolution Generative Adversarial Networks (DCGAN) to generate additional blueberry images.This aims to generate diverse blueberry images to enhance the datasets, thereby improving the model's capability for detecting and recognizing blueberries in complex field environments. Firstly, to reduce the number of parameters and improve the model's running speed, MobileNetV3 was employed as the backbone feature extraction network, which was conducive to detection in complex field environments. Secondly, to enhance the network's ability to extract features from blueberries, the Convolutional Block Attention Module (CBAM) was integrated into the backbone feature extraction network. Finally, by introducing SCYLLA-IoU (SIoU) as the bounding box regression loss function for YOLOv8n, the issue of angle mismatch between the ground truth and predicted boxes was addressed, further improving the accuracy of blueberry maturity identification. In terms of the backbone feature extraction network, compared with MobileNetV3, ShuffleNet, VanillaNet, and YOLOv8's own backbone CspdarkNet, the experimental results showed that MobileNetV3 as the backbone network of YOLOv8 had the highest mAP value. Additionally, among various attention mechanisms such as CBAM, SE, Shuffle Attention, and MHSA, CBAM demonstrated the best mAP performance. When assessing bounding box regression loss functions, SIoU outperformed GIoU, EIoU, and CIoU in terms of mAP. Through three key improvements, this study proposed a lightweight network model, MSC-YOLOv8, for blueberry fruit maturity detection,ensuring the real-time performance and accuracy. The results showed that the improved MSC-YOLOv8 model was 3.9 percentage points higher than that of YOLOv8. The average detection time was reduced by 3.97 ms compared with the original YOLOv8. Compared with SSD and CenterNet models, the improved model achieved better results on the blueberry dataset, and the average precision mean mAP is increased by 4.6 and 1.1 percentage points, respectively, which has advantages in detection speed and accuracy. This research provides technical support for blueberry picking robots to perform picking work under complex conditions in the fields. [ABSTRACT FROM AUTHOR]

Abstract:Semantic segmentation is a computer vision technique that requires extracting focused information from a large number of images and then transforming this information into a clearer and easierto- understand representation by means of a mask. Researchers are trying to find a balance in order to minimize the size of the model while ensuring its accuracy, which is currently a hot topic in designing lightweight network models. Currently, there are many challenges in image semantic segmentation techniques, such as segmentation discontinuity, incorrect segmentation, and high model complexity. To solve these problems, a lightweight semantic segmentation model based on attention mechanism is proposed. It uses freeze-thaw training, and the feature extraction network is MobileNetV2. To recover clearer target boundaries, a lightweight convolutional attention (CBAM) module is introduced in the output part of the atrous spatial pyramid pooling (ASPP) or channel attention (ECA-Net) in the decoding part. To solve the sample imbalance problem, the focal_loss loss function is introduced. Mixed accuracy is used, and the standard convolution in the output section is replaced with DO-Conv convolution. Experiments and validations are conducted on the PASCAL VOC2012 and Cityscapes datasets. The model size is 23.6 MB, with mean intersection over union (mIoU) scores of 73.91% and 74.89%, and class-wise pixel accuracy scores of 82.88% and 84.87% respectively. This successfully achieves a balance between accurate segmentation and computational efficiency. [ABSTRACT FROM AUTHOR]

This study proposes an embedded traffic sign detection system, YOLOV5-MCBS, based on an enhanced YOLOv5 algorithm. This system aims to mitigate the impact of traditional target detection algorithms' high computational complexity and low detection accuracy on traffic sign detection performance, thereby improving accuracy and real-time performance. Our primary objective is to develop a lightweight network that enhances detection accuracy, enabling real-time detection on embedded systems. First, to minimize computation and model size, we replaced the original YOLOv5 algorithm's backbone feature network with a lightweight MobileNetV3 network. Subsequently, we introduced the convolutional block attention module into the neck network to optimize the feature fusion stage's attention and enhance model detection accuracy. Concurrently, we employed the bidirectional feature pyramid network in the neck layer for multi-scale feature fusion. Additionally, we incorporated a small target detection layer into the original network output layer to enhance detection performance. What's more, we transplanted the enhanced algorithm into a Raspberry Pi embedded system to validate its real-time detection performance. Finally, we conducted computer simulations to assess our algorithm's performance by comparing it with existing target detection algorithms. Experimental results suggest that the enhanced algorithm achieves an average precision mean (mAP @ 0.5) value of 95.3% and frames per second value of 91.1 on the embedded system. [ABSTRACT FROM AUTHOR]

The field of computer vision has experienced rapid progress owing to deep learning. The importance of road damage detection in ensuring traffic safety and reducing road maintenance costs is becoming increasingly evident. For detecting road damage, the YOLOv5 algorithm provides a reliable and effective method. However, YOLOv5 still requires a significant amount of computation. This paper proposes a lightweight network for detecting road damage that improves upon the YOLOv5 model in four ways. The algorithm accurately identifies and classifies different types of road damage, while simultaneously reducing the number of parameters and required computations. First, lightweight processing of the model is achieved. The Ghost module and Ghost Bottleneck are employed to construct the novel GBS module and C3Ghost, which replace the existing CBS and C3 modules. Second, the CIoU loss function is transformed into SIoU to improve the precision of target box regression. Furthermore, the original upsampling module is replaced by CARAFE to improve the model's semantic adaptability and receptive field. Finally, the CBAM attention mechanism is employed to concentrate on crucial feature information. The experiment's findings present that, in comparison to the baseline model, the upgraded model has 41.8% fewer parameters. Additionally, there has been a 43.8% reduction in floating-point computation and an improvement of 0.2% in detection accuracy. [ABSTRACT FROM AUTHOR]

Traffic mark detection and identification play a key character in the development of driverless and intelligent transportation systems, offering significant assistance in ensuring the safety of people's daily travels. However, the detection effect of traffic signs is affected by many target categories, small targets, and low recognition accuracy, making traffic sign detection more challenging than target detection in general scenarios. In this paper, an improved YOLOv7 network (YOLOv7-COORD) is entered. Foremost, increase CBAM attention module at the connection between backbone and neck network of YOLOv7 to enhance the expression ability of neural networks through the attention mechanism, emphasizing important features and ignoring minor features to enhance the efficiency and precision of the network. Secondly, By adding CoordConv before the upsampling of the neck and before the detection head output, the network can better feel the location message in the characteristic map. Finally, a detection head generated by the low-level, high-resolution characteristic map is added to enhance the recognition accuracy of small target object. The abundance of experimental data demonstrates that the impression of the improved YOLOv7-COORD model is superior to that of the original YOLOv7 model, and the average accuracy of (mAP@0.5) on TT100K datasets is 3.2% higher than that of YOLOv7, reaching 85.4%. In summary, the improved YOLOv7-COORD model can better detect targets in traffic sign images. [ABSTRACT FROM AUTHOR]

Copyright of Wool Textile Journal is the property of National Wool Textile Science & Technology Information Center and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Addressing real-time aircraft target detection in microsatellite-based visible light remote sensing video imaging requires considering the limitations of imaging payload resolution, complex ground backgrounds, and the relative positional changes between the platform and aircraft. These factors lead to multi-scale variations in aircraft targets, making high-precision real-time detection of small targets in complex backgrounds a significant challenge for detection algorithms. Hence, this paper introduces a real-time aircraft target detection algorithm for remote sensing imaging using an improved lightweight attention mechanism that relies on the You Only Look Once version 7 (YOLOv7) framework (SE-CBAM-YOLOv7). The proposed algorithm replaces the standard convolution (Conv) with a lightweight convolutional squeeze-and-excitation convolution (SEConv) to reduce the computational parameters and accelerate the detection process of small aircraft targets, thus enhancing real-time onboard processing capabilities. In addition, the SEConv-based spatial pyramid pooling and connected spatial pyramid convolution (SPPCSPC) module extracts image features. It improves detection accuracy while the feature fusion section integrates the convolutional block attention module (CBAM) hybrid attention network, forming the convolutional block attention module Concat (CBAMCAT) module. Furthermore, it optimizes small aircraft target features in channel and spatial dimensions, improving the model's feature fusion capabilities. Experiments on public remote sensing datasets reveal that the proposed SE-CBAM-YOLOv7 improves detection accuracy by 0.5% and the mAP value by 1.7% compared to YOLOv7, significantly enhancing the detection capability for small-sized aircraft targets in satellite remote sensing imaging. [ABSTRACT FROM AUTHOR]

With the acceleration of urban development, the annual production of urban construction waste has been increasing yearly, which brings considerable challenges for urban supervision and management, and how to quickly and accurately identify construction waste is of great practical significance. In this paper, we propose a remote sensing image dust cover net construction waste recognition algorithm based on the improved U-network model to realize construction waste target recognition. The algorithm first prepares a dust cover net construction waste identification dataset using Google high-resolution remote sensing imagery as the database. Second, VGG16 is adopted as the backbone network of the U-Net model to improve the feature expression ability of the model. Finally, the Convolution Block Attention Module (CBAM) is embedded into the U-Net network to construct the CBAM-U-Net model to enhance the information extraction accuracy of high-resolution remote sensing images. With the remote sensing image encompassing Daxing District in Beijing as an example, the results show that the proposed algorithm can automatically and efficiently recognize the dust cover net construction waste with 95.51% recognition accuracy and 95.08% Mlou, which puts forward a new idea for the supervision of construction waste. [ABSTRACT FROM AUTHOR]

In view of the scarcity, high cost and lack of diversity of three-dimensional (3D) face datasets, this paper designs an end-to-end self-supervised learning 3D face reconstruction network, which uses single 2D face image as input. The model bypasses the 3D face datasets and only uses the 2D face datasets for training to achieve high-precision 3D face reconstruction without any 3D face prior. First, the improved ResNet50 feature extraction module is introduced to extract and characterize the input image by deep convolutional network. Then, a lightweight convolutional block attention module is added to the face prediction subnetwork. On the one hand, channel attention extracts different information included in the image, and on the other hand spatial attention finds the location of the information. So, the serialized attention operation could accurately find the features required for different parameter predictions, further improving face reconstruction parameters’ prediction accuracy. Finally, training, ablation and comparison experiments were conducted on CelebFaces Attributes, basel face model and Photoface datasets, and the combined loss function of pixel loss and perception loss was selected. The pixel loss function was calculated at the pixel microscopic level, and the perception loss function was calculated at the image macroscopic convolution level. The combination of the two could complement each other. Compared with the historical optimal results of the same network structure, the scale-invariant depth error and mean angle deviation of the proposed algorithm are improved by 5.2% and 8.2%, respectively. Experimental results strongly prove the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Central Asia is highly vulnerable to the impacts of climate change. There is, however, a lack of research on how local businesses, political elites and the general public perceive the need for comprehensive greening initiatives to address its consequences. To account for the material and ideational factors influencing clean energy transition in Central Asian petrostates, I offer an alternative analytic framework that I call ‘contrasting adaptation’. I argue that the energy transition has resulted in two contradictory responses at multiple levels of political governance: a formal response that is broadly supportive of new ‘green’ identities and policies, and an informal response that seeks to delay plans to decarbonize local business models and continues to prioritize ‘old’ oil and gas activities through new transnational networks. Drawing on a model of actor interactions at different levels of climate governance, I examine ‘contrasting adaptation’ – its constituent policies and discourse – to provide a better theorized framework for understanding climate politics in Central Asia. I use comparative analysis of the climate policies of Kazakhstan and Uzbekistan, the two Central Asian petrostates that pledged to carbon neutrality, informed by documentary and textual sources, and discourse analysis. [ABSTRACT FROM AUTHOR]

To address the high complexity and low accuracy issues of traditional methods in mixed coal vitrinite identification, this paper proposes a method based on an improved DeepLabv3+ network. First, MobileNetV2 is used as the backbone network to reduce the number of parameters. Second, an atrous convolution layer with a dilation rate of 24 is added to the ASPP (atrous spatial pyramid pooling) module to further increase the receptive field. Meanwhile, a CBAM (convolutional block attention module) attention mechanism with a channel multiplier of 8 is introduced at the output part of the ASPP module to better filter out important semantic features. Then, a corrective convolution module is added to the network's output to ensure the consistency of each channel's output feature map for each type of vitrinite. Finally, images of 14 single vitrinite components are used as training samples for network training, and a validation set is used for identification testing. The results show that the improved DeepLabv3+ achieves 6.14% and 3.68% improvements in MIOU (mean intersection over union) and MPA (mean pixel accuracy), respectively, compared to the original DeepLabv3+; 12% and 5.3% improvements compared to U-Net; 9.26% and 4.73% improvements compared to PSPNet with ResNet as the backbone; 5.4% and 9.34% improvements compared to PSPNet with MobileNetV2 as the backbone; and 6.46% and 9.05% improvements compared to HRNet. Additionally, the improved ASPP module increases MIOU and MPA by 3.23% and 1.93%, respectively, compared to the original module. The CBAM attention mechanism with a channel multiplier of 8 improves MIOU and MPA by 1.97% and 1.72%, respectively, compared to the original channel multiplier of 16. The data indicate that the proposed identification method significantly improves recognition accuracy and can be effectively applied to mixed coal vitrinite identification. [ABSTRACT FROM AUTHOR]

In response to the challenges of accurate identification and localization of garbage in intricate urban street environments, this paper proposes EcoDetect-YOLO, a garbage exposure detection algorithm based on the YOLOv5s framework, utilizing an intricate environment waste exposure detection dataset constructed in this study. Initially, a convolutional block attention module (CBAM) is integrated between the second level of the feature pyramid etwork (P2) and the third level of the feature pyramid network (P3) layers to optimize the extraction of relevant garbage features while mitigating background noise. Subsequently, a P2 small-target detection head enhances the model's efficacy in identifying small garbage targets. Lastly, a bidirectional feature pyramid network (BiFPN) is introduced to strengthen the model's capability for deep feature fusion. Experimental results demonstrate EcoDetect-YOLO's adaptability to urban environments and its superior small-target detection capabilities, effectively recognizing nine types of garbage, such as paper and plastic trash. Compared to the baseline YOLOv5s model, EcoDetect-YOLO achieved a 4.7% increase in mAP0.5, reaching 58.1%, with a compact model size of 15.7 MB and an FPS of 39.36. Notably, even in the presence of strong noise, the model maintained a mAP0.5 exceeding 50%, underscoring its robustness. In summary, EcoDetect-YOLO, as proposed in this paper, boasts high precision, efficiency, and compactness, rendering it suitable for deployment on mobile devices for real-time detection and management of urban garbage exposure, thereby advancing urban automation governance and digital economic development. [ABSTRACT FROM AUTHOR]

Landslide event detection poses a significant challenge in the remote sensing community, especially with the advancements in computer vision technology. As computational capabilities continue to grow, the traditional manual and partially automated methods of landslide recognition from remote sensing data are transitioning towards automatic approaches using deep learning algorithms. Moreover, attention models, encouraged by the human visual system, have emerged as crucial modules in diverse applications including natural hazard assessment. Therefore, we suggest a novel and intelligent generalized efficient layer aggregation network (GELAN) based on two prevalent attention modules, efficient channel attention (ECA) and convolutional block attention module (CBAM), to enrich landslide detection techniques from satellite images. CBAM and ECA are separately integrated into GELAN at different locations. The experiments are conducted using satellite images of the Nepal Himalayan region. Standard metrics such as precision, recall, F-score, and mAP (mean average precision) are considered for quantitative evaluation. GELANc+CBAM (F-score = 81.5%) demonstrates the best performance. This study underscores the suitability of the proposed approach in up-to-date inventory creation and accurate landslide mapping for disaster recovery and response efforts. Moreover, it contributes to developing early prediction models for landslide hazards. [ABSTRACT FROM AUTHOR]

This study addresses the challenges of low detection precision and limited generalization across various ripeness levels and varieties for large non-greenripe citrus fruits in complex scenarios. We present a high-precision and lightweight model, YOLOC-tiny, built upon YOLOv7, which utilizes EfficientNet-B0 as the feature extraction backbone network. To augment sensing capabilities and improve detection accuracy, we embed a spatial and channel composite attention mechanism, the convolutional block attention module (CBAM), into the head's efficient aggregation network. Additionally, we introduce an adaptive and complete intersection over union regression loss function, designed by integrating the phenotypic features of large non-green-ripe citrus, to mitigate the impact of data noise and efficiently calculate detection loss. Finally, a layerbased adaptive magnitude pruning strategy is employed to further eliminate redundant connections and parameters in the model. Targeting three types of citrus widely planted in Sichuan Province--navel orange, Ehime Jelly orange, and Harumi tangerine--YOLOC-tiny achieves an impressive mean average precision (mAP) of 83.0%, surpassing most other state-of-the-art (SOTA) detectors in the same class. Compared with YOLOv7 and YOLOv8x, its mAP improved by 1.7% and 1.9%, respectively, with a parameter count of only 4.2M. In picking robot deployment applications, YOLOC-tiny attains an accuracy of 92.8% at a rate of 59 frames per second. This study provides a theoretical foundation and technical reference for upgrading and optimizing low-computing-power ground-based robots, such as those used for fruit picking and orchard inspection. [ABSTRACT FROM AUTHOR]

The Carbon Border Adjustment Mechanism (CBAM) proposed by the European Union (EU), commonly known as the ‘carbon tariff,’ is a prominent issue in international governance and has received significant social attention. The EU has completed CBAM legislation and begun concrete implementation. The United Kingdom and United States have proposed similar bills, and the G7 countries have collaborated to lead the establishment of the International Climate Club, which has exhibited trends of legalization, institutionalization, and internationalization. Recently, numerous studies on CBAM have emerged in China, mostly focusing on its direct economic impacts. However, there has been insufficient understanding of the political impacts, a lack of analysis of the logic and inherent flaws of CBAM from a carbon equity perspective, and relatively weak research on synergistic international governance to address the issue. This paper attempts to carry out research on CBAM from the carbon equity perspective, with the following main conclusions: ① The development history of CBAM in the international climate governance process can be divided into four stages: theoretical foresight, brewing and preparation, pilot obstruction, and legislative implementation. This process has demonstrated a trajectory from concept to policy and then to implementation, presenting the characteristics of continuous legalization, institutionalization, and internationalization. ② The EU's logical starting point for the implementation of CBAM lies in carbon leakage. Clarifying the connotation of carbon leakage helps reveal the CBAM's inherent flaws. According to the main conclusions of the Intergovernmental Panel on Climate Change (IPCC) assessment reports, the basic consensus of the international academic community on carbon leakage is that there is no evidence that significant carbon leakage exists, and carbon tariffs are ineffective in reducing it. ③ The CBAM has impacts extending beyond economic aspects, and more importantly, it has had multiple impacts on existing international climate governance in terms of concepts, principles, methods, and objectives. The deep-rooted cause of the CBAM lies in the fragmentation of the international climate governance and international economic and trade governance systems, as well as the conflicting perception of equity among all parties. ④ To resolve the conflicts of CBAM, it is necessary to strengthen international synergistic governance from a carbon equity perspective. Possible strategies include: enhancing dialogue and exchange of the concept of equity among countries considering the new global development challenges; strengthening communication and coordination between international climate governance and international economic and trade governance with the UNFCCC as the main platform; initiating substantive negotiations on carbon tariffs under the UNFCCC; and promoting global value chain development through international cooperation. [ABSTRACT FROM AUTHOR]

This study aims to solve the low detection accuracy and susceptibility to false detection and omission in pedestrian and vehicle detection by proposing an improved YOLOv5s algorithm. Firstly, a small target detection module is added to better acquire and determine the information of pedestrians from longrange vehicles. Secondly, the multi-scale channel attention CBAM attention module is added, and the dual attention mechanism is not only flexible and convenient, but also improves the computational efficiency. Finally, the MPDIoU loss function based on minimum point distance is introduced to replace the original GIoU loss function, and this change not only enhances the regression accuracy of the model. At the same time, the convergence speed of the model is accelerated. KITTI data set was used for experiments, and the experimental results showed that the average accuracy of the model trained by the improved YOLOv5s algorithm on the data set reached 84.9%, which was 3.7% higher than that of the original YOLOv5s algorithm. It is verified that the model is suitable for high accuracy of pedestrian and vehicle recognition in complex environments, and has high value for promotion. [ABSTRACT FROM AUTHOR]

In the continuous working environment of the picking robots, factors such as illumination change, camera hardware, the movement of the picking robots, and image background interference have a great impact on the tomato detection accuracy of the picking robots. At present, the research on continuous working environment has not effectively solved the problem that multiple environmental factors affect tomato recognition. To solve this problem, based on YOLO V5s, this paper proposes an improved YOLO V5s for tomato identification in the continuous working environment of picking robots. The improved YOLO V5s are improved from the following aspects. Firstly, image sample data in continuous work environment is enriched by a data enhancement method. In addition, CBAM attention mechanism module is added to YOLO V5s feature extraction network to improve the capability of tomato feature extraction in the continuous work environment. On this basis, in the prediction part of YOLO V5s network model, an improved Soft Non-Maximum Suppression (Soft-NMS) method was proposed to improve the recognition ability of the network model to cluster tomatoes in the continuous working environment. The test results show that the tomato detection precision and recall rate of the improved YOLO V5s model are 92.08% and 82.42% respectively, the mAP is 92.75%. Compared with the original model, the improved YOLO V5s recognition precision increased by 2.72% and mAP increased by 1.29%. It has strong robustness under different lighting conditions and different tomatoes blocking each other, and can be better used in the continuous working environment of the picking robots. [ABSTRACT FROM AUTHOR]

The increase in fire accidents caused by indoor charging of electric bicycles has raised concerns among people. Monitoring EBs in elevators is challenging, and the current object detection method is a variant of YOLOv5, which faces problems with calculating the load and detection rate. To address this issue, this paper presents an improved lightweight method based on YOLOv5s to detect EBs in elevators. This method introduces the MobileNetV2 module to achieve the lightweight performance of the model. By introducing the CBAM attention mechanism and the Bidirectional Feature Pyramid Network (BiFPN) into the YOLOv5s neck network, the detection precision is improved. In order to better verify that the model can be deployed at the edge of an elevator, this article deploys it using the Raspberry Pi 4B embedded development board and connects it to a buzzer for application verification. The experimental results demonstrate that the model parameters of EBs are reduced by 58.4%, the computational complexity is reduced by 50.6%, the detection precision reaches 95.9%, and real-time detection of electric vehicles in elevators is achieved. [ABSTRACT FROM AUTHOR]