Search Results (8)

Cybersecurity is established as fundamental for organisations and individuals engaging with digital technology. A central topic in cybersecurity is user behaviour, which has been shown to be the root cause or enabler in a majority of all cyber incidents with a resultant need to empower users to adopt secure behaviour. Researchers and practitioners agree that a crucial step in empowering users to adopt secure behaviour is training. Subsequently, there are many different methods for cybersecurity training discussed in the scientific literature and that are adopted in practise. However, research suggests that those training efforts are not effective enough, and one commonly mentioned reason is user adoption problems. In essence, users are not engaging with the provided training to the extent needed to benefit from the training as expected. While the perception and adoption of individual training methods are discussed in the scientific literature, cohesive studies on the factors that impact user adoption are few and far between. To that end, this paper focuses on the user acceptance of cybersecurity training using the technology acceptance model as a theory base. Based on 22 included publications, the research provides an overview of the cybersecurity training acceptance factors that have been discussed in the existing scientific literature. The main contributions are a cohesive compilation of existing knowledge about factors that impact the user acceptance of cybersecurity training and the introduction of the CTAM, a cybersecurity training acceptance model which pinpoints four factors—regulatory control, worry, apathy, and trust—that influence users’ intention to adopt cybersecurity training. The results can be used to guide future research as well as to guide practitioners implementing cybersecurity training. Full article

In the context of rapidly advancing agricultural technology, precise and efficient methods for crop detection and counting play a crucial role in enhancing productivity and efficiency in crop management. Monitoring corn tassels is key to assessing plant characteristics, tracking plant health, predicting yield, and addressing issues such as pests, diseases, and nutrient deficiencies promptly. This ultimately ensures robust and high-yielding corn growth. This study introduces a method for the recognition and counting of corn tassels, using RGB imagery captured by unmanned aerial vehicles (UAVs) and the YOLOv8 model. The model incorporates the Pconv local convolution module, enabling a lightweight design and rapid detection speed. The ACmix module is added to the backbone section to improve feature extraction capabilities for corn tassels. Moreover, the CTAM module is integrated into the neck section to enhance semantic information exchange between channels, allowing for precise and efficient positioning of corn tassels. To optimize the learning rate strategy, the sparrow search algorithm (SSA) is utilized. Significant improvements in recognition accuracy, detection efficiency, and robustness are observed across various UAV flight altitudes. Experimental results show that, compared to the original YOLOv8 model, the proposed model exhibits an increase in accuracy of 3.27 percentage points to 97.59% and an increase in recall of 2.85 percentage points to 94.40% at a height of 5 m. Furthermore, the model optimizes frames per second (FPS), parameters (params), and GFLOPs (giga floating point operations per second) by 7.12%, 11.5%, and 8.94%, respectively, achieving values of 40.62 FPS, 14.62 MB, and 11.21 GFLOPs. At heights of 10, 15, and 20 m, the model maintains stable accuracies of 90.36%, 88.34%, and 84.32%, respectively. This study offers technical support for the automated detection of corn tassels, advancing the intelligence and precision of agricultural production and significantly contributing to the development of modern agricultural technology. Full article

Object detection in remote sensing scenarios plays an indispensable and significant role in civilian, commercial, and military areas, leveraging the power of convolutional neural networks (CNNs). Remote sensing images, captured by crafts and satellites, exhibit unique characteristics including complicated backgrounds, limited features, distinct density, and varied scales. The contextual and comprehensive information in an image can make a detector precisely localize and classify targets, which is extremely valuable for object detection in remote sensing scenarios. However, CNNs, restricted by the essence of the convolution operation, possess local receptive fields and scarce contextual information, even in large models. To address this limitation and improve detection performance by extracting global contextual information, we propose a novel plug-and-play attention module, named Convolution with Transformer Attention Module (CTAM). CTAM is composed of a convolutional bottleneck block and a simplified Transformer layer, which can facilitate the integration of local features and position information with long-range dependency. YOLOv8n, a superior and faster variant of the YOLO series, is selected as the baseline. To demonstrate the effectiveness and efficiency of CTAM, we incorporated CTAM into YOLOv8n and conducted extensive experiments on the DIOR dataset. YOLOv8n-CTAM achieves an impressive 54.2 mAP@50-95, surpassing YOLOv8n (51.4) by a large margin. Notably, it outperforms the baseline by 2.7 mAP@70 and 4.4 mAP@90, showcasing its superiority with stricter IoU thresholds. Furthermore, the experiments conducted on the TGRS-HRRSD dataset validate the excellent generalization ability of CTAM. Full article

The efficient detection of crew overboard behavior has become an important element in enhancing the ability to respond to marine disasters. It remains challenging due to (1) the lack of effective features making feature extraction difficult and recognition accuracy low and (2) the insufficient computing power resulting in the poor real-time performance of existing algorithms. In this paper, we propose a Channel-Time Dense Residual Network (CTDR-Net) for detecting crew overboard behavior, including a Dense Residual Network (DR-Net) and a Channel-Time Attention Mechanism (CTAM). The DR-Net is proposed to extract features, which employs the convolutional splitting method to improve the extraction ability of sparse features and reduce the number of network parameters. The CTAM is used to enhance the expression ability of channel feature information, and can increase the accuracy of behavior detection more effectively. We use the LeakyReLU activation function to improve the nonlinear modeling ability of the network, which can further enhance the network’s generalization ability. The experiments show that our method has an accuracy of 96.9%, striking a good balance between accuracy and real-time performance. Full article

The large volumetric expansion of conversion-type anode materials (CTAMs) based on transition-metal oxides is still a big challenge for lithium-ion batteries (LIBs). An obtained nanocomposite was established by tin oxide (SnO₂) nanoparticles embedding in cellulose nanofiber (SnO₂-CNFi), and was developed in our research to take advantage of the tin oxide’s high theoretical specific capacity and the cellulose nanofiber support structure to restrain the volume expansion of transition-metal oxides. The nanocomposite utilized as electrodes in lithium-ion batteries not only inhibited volume growth but also contributed to enhancing electrode electrochemical performance, resulting in the good capacity maintainability of the LIBs electrode during the cycling process. The SnO₂-CNFi nanocomposite electrode delivered a specific discharge capacity of 619 mAh g⁻¹ after 200 working cycles at the current rate of 100 mA g⁻¹. Moreover, the coulombic efficiency remained above 99% after 200 cycles showing the good stability of the electrode, and promising potential for commercial activity of nanocomposites electrode. Full article

Recently, multi-level feature networks have been extensively used in instance segmentation. However, because not all features are beneficial to instance segmentation tasks, the performance of networks cannot be adequately improved by synthesizing multi-level convolutional features indiscriminately. In order to solve the problem, an attention-based feature pyramid module (AFPM) is proposed, which integrates the attention mechanism on the basis of a multi-level feature pyramid network to efficiently and pertinently extract the high-level semantic features and low-level spatial structure features; for instance, segmentation. Firstly, we adopt a convolutional block attention module (CBAM) into feature extraction, and sequentially generate attention maps which focus on instance-related features along the channel and spatial dimensions. Secondly, we build inter-dimensional dependencies through a convolutional triplet attention module (CTAM) in lateral attention connections, which is used to propagate a helpful semantic feature map and filter redundant informative features irrelevant to instance objects. Finally, we construct branches for feature enhancement to strengthen detailed information to boost the entire feature hierarchy of the network. The experimental results on the Cityscapes dataset manifest that the proposed module outperforms other excellent methods under different evaluation metrics and effectively upgrades the performance of the instance segmentation method. Full article

In this research paper a new conceptual model is introduced—the Diagnosis of Communication and Trust in Aviation Maintenance (DiCTAM) model. The purpose of this model is to recognise, measure and predict the relationship between communication and trust in the aviation maintenance field. This model was formed by combining a conceptual cyclical process and two established survey tools, adapted and incorporated in a single question set. The implementation of each phase of the DiCTAM model is performed with the use of qualitative and quantitative research methods. This includes the use of content analyses of accident/incident investigation reports and training material, a survey and a hypothetical case study. The predictive functionality of the DiCTAM model has been investigated through the hypothetical case study. The obtained results indicate a positive relationship between communication and trust according to the aviation maintenance employees’ perception and accidents/incidents reports, even though basic training includes communication without direct mention of trust. Full article

Cement-treated aggregate mixture (CTAM) is widely used in many countries. To design this mixture using the vibration compaction method brings about many problems, such as serious inconsistencies in key parameters and strong vibration energy and noise imposed on adjacent buildings and people. This work presents a preliminary investigation of the use of Superpave gyratory compactor, which has been widely used to compact hot mix asphalt in the laboratory, to design CTAM. The 3-2-2 mode of the locking point was used to determine that the gyration compaction number N_design should be 105. The performances of the CTAM specimens prepared using gyration compaction were compared with those prepared using the Proctor and vibration compaction methods. Compared with Proctor and vibration compaction, gyration compaction had a smaller influence on aggregate degradation. Also, the optimal moisture content after gyration compaction was the minimum. The index values for maximum dry density, unconfined compressive strength and dry/temperature shrinkage coefficient after gyration compaction were between those for Proctor compaction and vibration compaction. It can be concluded that it is feasible to design CTAM by using a Superpave gyratory compactor to compact the mixture for 105 cycles. Full article