Pavement crack detection is one of the key links in highway pavement maintenance management. In the current road crack detection, the network model easily ignores the shallow geometric features of cracks and cannot extract the key feature information of cracks. Aiming at the above problems, an EC-YOLOX network model is proposed. The CFPN is constructed to cross-fuse different scale features to solve the feature scale invariance. In the strengthen feature extraction layer, ECANet is fused to enhance the ability to pay attention to key information. The WEIoU loss function is proposed, which assigns different penalty terms to the target box in the vertical and horizontal directions. In terms of data, geometric distortion constraint correction is performed on the single-point perspective pavement image. The experimental data show that the accuracy of EC-YOLOX on the self-built data set reaches 76.34% mAP@0.5, which is 4.45% higher than that of YOLOX. The loss curve of EC-YOLOX is smoother than that of YOLOX, and the minimum training loss value is 2.136, which is 0.648 lower than the minimum loss value 2.784 of YOLOX. Many experiments have verified the effectiveness of EC-YOLOX in improving the detection effect in pavement crack detection.

This research introduces a pioneering solution to the challenges posed by gastrointestinal tract (GI) cancer in radiation therapy, focusing on the imperative task of precise organ segmentation for minimizing radiation-induced damage. GI imaging has historically used manual demarcation, which is laborious and uncomfortable for patients. We address this by introducing the ResECA-U-Net deep learning model, a novel combination of the UNet and ResNet34 architectures. Furthermore, we further augment its functionality by incorporating the Efficient Channel Attention (ECA-Net) methodology. By utilizing data from the UW-Madison Carbone Cancer Centre, we carefully investigate several image processing techniques designed to capture critical local characteristics. With its foundation in computer vision concepts, the ResECA-U-Net model is excellent at extracting fine details from GI images. Sophisticated metrics such as intersection over union (IoU) and the dice coefficient are used to evaluate performance. Our study's outcomes demonstrate the effectiveness of the suggested method, yielding an impressive 96.27% Dice coefficient and 91.48% IoU. These results highlight the significant contribution that our strategy has made to the advancement of cancer therapy. Beyond its scientific merits, this work has the potential to significantly enhance cancer patients' quality of life and provide better long-term outcomes. Our work is a significant step towards automating and optimizing the segmentation process, which can potentially change how GI cancer is treated completely.

Responding to an urgent call for effective and cost-efficient enforcement of emission regulations from ocean-going vessels (OGVs) at sea, the Royal Belgian Institute of Natural Sciences, one of the 17 official partners of the Belgian coastguard structure, launched its airborne sniffer program in 2015. A custom-built sniffer sensor was installed on board of the Belgian coastguard aircraft and has been used for the in-situ measurement of SO2 and NOx in OGV exhausts, providing non-compliance alerts to port inspection authorities in Belgium and other EU member states. To guarantee the consistency in quality and reliability of the airborne non-compliance alerts, the standard operational procedures and measurement methodology have been described and recorded in a sniffer quality management system (SQMS). As part of the SQMS, alerting thresholds for non-compliance with SO2 and NOx standards were defined based on the emission limits and the measurement uncertainty. In addition, compliant measurements for the fuel sulfur content (FSC) were shared through Thetis-EU, which is the sulfur inspection database hosted by the European Maritime Safety Agency (EMSA). By providing both non-compliance alerts and compliant measurements to port inspection authorities, the coastguard aircraft fulfills a first-step in the enforcement chain. This article demonstrates that the reported alerts have not only led to several sanctions, but the analysis also showed that the provision of airborne alerts improved the efficiency of the port inspection authorities drastically, resulting in a cost-efficient improvement of their enforcement strategy. Port inspection authorities were able to follow-up on 46% of the generated FSC alerts. In addition, 43% of the alerted OGVs, which were followed up, were confirmed as non-compliant after a port inspection. Accordingly, conditions were in place to effectively sanction 20% of the non-compliant observations. For NOx alerts, a limited follow-up was done by port inspection authorities and none of the alerts were confirmed by those inspections, which is mainly due to the lack of suitable inspection mechanisms and the absence of additional EU implementation regulations on NOx emission inspections. Aiming at paving the way for a stricter enforcement of airborne non-compliance alerts, a validation analysis was done between the airborne FSC measurements and reference FSC measurements. In addition to onboard measurements from exhaust gas cleaning systems (EGCS), analyzes of fuel samples were also carried out by port inspection authorities in order to acquire reference measurements. The validation analysis presented in this study showed that the empiric deviation of the airborne FSC measurements with the reference measurements was significantly lower than the uncertainty used in the reporting thresholds. It has also been shown that aerial measurements provide evidence-based data that, given an appropriate confidence interval, has the potential to be considered reliable legal evidence.

Air pollution is a leading cause of death worldwide, and has a profound impact on the planet's climate and ecosystems. A substantial portion of air pollution is attributable to Ocean Going Vessels (OGVs). In light of this, international regulations have been put in place to mitigate air pollutant emissions from OGVs. While studies have indicated that these regulations can create significant health, environmental, and economic benefits, there remains a research gap regarding their specific impact on enhancing air quality. The aim of this study is to investigate how the implemented regulations have affected air quality in the Southern North Sea. The study found that the international regulations on ship emissions have successfully led to a decline in SO2 emissions from OGVs in the Southern North Sea, which resulted in a reduction of ambient SO2 concentrations inland, leading to positive effects on public health and the environment. However, it was projected that their proportion is tend to increase in the following years. Moreover, the study revealed that Exhaust Gas Cleaning Systems (EGCS) present significant concerns. They were significantly more frequently found to be non-compliant, and more alarmingly, they emit notably higher average levels of SO2. It also turned out that international regulations in the southern North Sea have less effect on the reduction of NOx emissions from OGVs than expected. The NOx emissions from OGVs will even account for an alarming 40% of the total domestic NOx emissions for the Belgian Region by 2030.