Polycyclic aromatic hydrocarbons in ship breaking area and associated ecological risk assessment: evidence from the Sitakund ship-breaking area in Bangladesh.

Ship-breaking yards are recognized for releasing hazardous polycyclic aromatic hydrocarbons (PAHs), leading to severe environmental pollution in the sediment of ship-breaking areas. This study assessed the concentrations of 16 priority PAHs in surface sediments collected from the intertidal zone adjacent to the Sitakund ship-breaking yards. The samples underwent Soxhlet extraction and detection using PerkinElmer GC-Clarus 690 and MS-Clarus SQ8C with an Elite-5MS capillary column (30 m × 0.25 mm ID × 0.25 µm). The study utilized PAH concentrations to reveal spatial distribution patterns, identify point sources, and assess potential toxicity. The total PAH concentration ranged from 1899.2 to 156,800.08 ng g-1 dw, while the concentration of 7 carcinogenic PAHs ranged from 822.03 to 1899.15 ng g-1 dw. High molecular weight PAHs dominated among the 16 PAHs, whereas low molecular weight PAHs, such as 2-ring PAHs, were negligible. Source characterization based on different molecular ratios suggested that PAHs in the area originated from pyrolytic processes related to ship dismantling, fishing activities, and water transportation for people. The observed PAH concentrations exceeded both national and international standards for sedimentary PAH levels, indicating significant ecological risks. The total TEQcarc values of sediment samples varied from 564.41 to 10,695.12 ng g-1, with a mean value of 3091.25 ng g-1. The study's findings underscore the immediate biological damage that PAH contamination in the Sitakund ship-breaking area could cause, emphasizing the need for effective control measures to ensure ecological and human safety.

Infrared Bilateral Polarity Ship Detection in Complex Maritime Scenarios.

In complex maritime scenarios where the grayscale polarity of ships is unknown, existing infrared ship detection methods may struggle to accurately detect ships among significant interference. To address this issue, this paper first proposes an infrared image smoothing method composed of Grayscale Morphological Reconstruction (GMR) and a Relative Total Variation (RTV). Additionally, a detection method considering the grayscale uniformity of ships and integrating shape and spatiotemporal features is established for detecting bright and dark ships in complex maritime scenarios. Initially, the input infrared images undergo opening (closing)-based GMR to preserve dark (bright) blobs with the opposite suppressed, followed by smoothing the image with the relative total variation model to reduce clutter and enhance the contrast of the ship. Subsequently, Maximally Stable Extremal Regions (MSER) are extracted from the smoothed image as candidate targets, and the results from the bright and dark channels are merged. Shape features are then utilized to eliminate clutter interference, yielding single-frame detection results. Finally, leveraging the stability of ships and the fluctuation of clutter, true targets are preserved through a multi-frame matching strategy. Experimental results demonstrate that the proposed method outperforms ITDBE, MRMF, and TFMSER in seven image sequences, achieving accurate and effective detection of both bright and dark polarity ship targets.

Causal diagnostic model and governance strategies to reduce pollution from ship accidents in Chinese waters.

Ship transportation is a primary mode for global trade and cargo transport, yet even minor discrepancies can lead to ship accidents, causing severe secondary environmental pollution. Maritime accidents involve complex and numerous factors. Formal Concept Analysis (FCA) can identify the key contributing factors and their impact levels by eliminating homogenization factors in maritime accidents. This study constructs an innovative FCA model of ship accidents in Chinese waters, utilizing 172 ship accident reports released by the China Maritime Safety Administration. The analysis reveals seven reduced sets and 23 diagnostic rules of ship accidents. Results show that failed ship registration/security inspection, deficient nautical data and instruments, and management issues are the most critical factors. Three accident chains are identified and corresponding mitigation strategies are proposed to reduce potential pollution from ship accidents. These strategies offer significant reference value for preventing ship accidents and reducing their environmental impact in China and globally.

Evolution of inclusions in DH36 grade ship plate steel during high heat input welding.

In this paper, the solid solution and precipitation behavior of inclusions on the surface and 1/2 thickness of the tested steel plate under the condition of welding heat input of 400 kJ/cm is investigated by using laser confocal experiments with hot-rolled state DH36 ship plate steel as the research object, and the mechanism of the effect of inclusions on the phase transformation of an acicular ferrite is revealed. The results show that the inclusions of the tested steel are mainly composed of Oxide-MnS, MnS, Oxide, TiN, Spinel, etc. The amount of inclusions on the surface of the tested steel plate is significantly higher than that at the 1/2 thickness position. During the heating stage, the small inclusions on the surface immediately disappeared, and the large inclusions gradually solidified in the matrix; atomic diffusion occurred at the bond between the inclusions and the matrix; while the small inclusions at the 1/2 thickness position gradually disappeared at the beginning of the heating stage, and the inclusions began to precipitate and grow when the temperature was increased to 990 °C. The acicular ferrite preferentially nucleates and grows near the boundary of the inclusions during the post-weld cooling stage, and its growth ends when two acicular ferrites cross.

A new ship tracing technology from oil spills based on multi-source data.

Oil spill from ship can cause serious pollution to the Marine environment, but it is very difficult to find and confirm the troublemaker. In order to determine the oil spill ship, this paper proposes a new method to trace the source of ship oil spills and find the suspected ship that spills oil based on SAR imagery, AIS data and related marine environment data. First, we filter AIS data based on position of oil spill areas on remote sensing imagery and convert oil spill areas into trajectory points. Secondly, based on the Lagrangian particle motion model, a bidirectional drift model is proposed to calculate the average similarity between the forward and backward drift results. Finally, the most likely oil spill ship is determined according to the average similarity results. The results of the case study show that the method is effective and practical.

Mothership versus Drip-and-Ship for stroke in a rural area: A French prospective observational study.

BACKGROUND: The availability of mechanical thrombectomy (MT) is limited. Thus, there are two paradigms for patients living closer to a primary stroke center (PSC) than a comprehensive stroke center (CSC) capable of MT: "Mothership" (direct referral to a CSC) and "Drip-and-Ship" (referral to a PSC for imaging and thrombolysis and transfer to a CSC for thrombectomy or monitoring). We aimed to compare the prognosis of patients at three months between the two paradigms in a rural area. MATERIALS: From September 2019 to March 2021, we prospectively included patients living closer to a PSC than the one CSC, regardless of the type of stroke or reperfusion treatment. The proportion of patients with a good functional outcome (Rankin≤2) at three months was compared between the two initial orientations for all patients and for subgroups: patients with ischemic stroke and patients treated by MT. RESULTS: Among the 206 patients included, 103 were admitted directly to the CSC (82.5% had an ischemic stroke and 24.3% a MT) and 103 initially admitted to a PSC and then transferred to the CSC (100% had an ischemic stroke and 52.4% a MT). The proportion of patients with a good outcome was comparable between the two groups (54.5% vs. 43.7%, P=0.22). Among the 79 patients who underwent MT, the prognosis at three months was better in the Mothership group (49.3% vs. 15.3%, P=0.01). CONCLUSION: The functional prognosis is comparable between Mothership and Drip-and-Ship paradigms in our setting, despite a trend towards a better prognosis for the Mothership. As has been shown in urban settings, the mothership paradigm also leads to a better prognosis for patients treated with MT in a rural setting.

Split_ Composite: A Radar Target Recognition Method on FFT Convolution Acceleration.

Synthetic Aperture Radar (SAR) is renowned for its all-weather and all-time imaging capabilities, making it invaluable for ship target recognition. Despite the advancements in deep learning models, the efficiency of Convolutional Neural Networks (CNNs) in the frequency domain is often constrained by memory limitations and the stringent real-time requirements of embedded systems. To surmount these obstacles, we introduce the Split_ Composite method, an innovative convolution acceleration technique grounded in Fast Fourier Transform (FFT). This method employs input block decomposition and a composite zero-padding approach to streamline memory bandwidth and computational complexity via optimized frequency-domain convolution and image reconstruction. By capitalizing on FFT's inherent periodicity to augment frequency resolution, Split_ Composite facilitates weight sharing, curtailing both memory access and computational demands. Our experiments, conducted using the OpenSARShip-4 dataset, confirm that the Split_ Composite method upholds high recognition precision while markedly enhancing inference velocity, especially in the realm of large-scale data processing, thereby exhibiting exceptional scalability and efficiency. When juxtaposed with state-of-the-art convolution optimization technologies such as Winograd and TensorRT, Split_ Composite has demonstrated a significant lead in inference speed without compromising the precision of recognition.

Monetizing shipping emission reduction: Environmental benefit analysis of domestic emission control areas policy 2.0 in China.

Shipping is a major contributor to anthropogenic emissions, exerting complex effects on both the environment and climate. To improve the air quality of coastal areas, China adopted an upgraded policy on domestic emission control areas (DECA 2.0) for shipping in December 2018, which expanded the geographical scope of ECAs from three designated heavy-traffic coastal regions to the entire 12 nautical mile-territorial seas and also introduced more stringent ship emission requirements. Based on data from the Automatic Identification System, this study first evaluates the environmental effects of ship emissions' reduction brought by DECA policy 2.0. Results reveal that implementation of DECA policy 2.0 has resulted in a cumulative reduction (2019-2021) of 8.43 × 105 tons, 1.3 × 105 tons, and 1.49 × 105 tons of sulfur dioxide (SO2), particulate matter <2.5 µm diameter (PM2.5) and PM <10 µm (PM10) emissions, respectively. Based on the external cost method, we further monetize the environmental benefits arising from reduction of air pollution emissions, averaging $3.6 billion USD per year. This number equates to ca. 4 % of the total output value of China's marine transportation industry over the three-year period. Finally, we calculate the fuel replacement cost arising from the implementation of DECA policy 2.0, which is on average $1.23 billion USD. This indicates that the net environmental benefits of DECA policy 2.0 equate nearly double the associated costs.

Investigation of emission characteristics of a marine cargo ship in real-world conditions.

Pollution caused by ship emissions will considerably impact coastal areas. A test system that matched the actual conditions of a ship was designed based on a portable emission measurement system (PEMS), and the emission characteristic of gaseous and particle emissions and the particle size distribution of the ship's main engine were investigated under real-world operating conditions. The results showed that the emission concentrations of the main pollutants fluctuated greatly under the departure, anchoring, and docking conditions, and the peaks of CO, CO2, and NOx emissions appeared under these transient conditions. The emission concentrations of CO2, hydrocarbons, particle number (PN), and particulate mass increased with the increase in speed. The PN-based particle size distribution of the engine presented a unimodal distribution under daily operating conditions. The maximum emission factor of NOx based on the engine power was 29.53 g/kWh at the engine speed of 66 r/min. The results of the study may contribute to supplementing the emission factors of this type of ship, and provide data support for monitoring and assessment of the marine environment.

Coexistence of roof rats and carnivores in barns on a livestock farm in Japan.

Brown rats (Rattus norvegicus), roof rats (Rattus rattus), and house mice (Mus musculus) are considered to be important pests on livestock farms. Although the diel activity patterns of rodents are key to their control, information on this aspect of their ecology is limited. Furthermore, the effect of carnivores on rodent activity patterns as well as the carnivore species present on livestock farms is unclear. Here, we set camera traps in an open-type cow barn and in an enclosed pig barn on the same livestock farm in Japan from August through October 2021. The only rodents observed in both barns were roof rats, and the carnivore species observed were dogs (Canis familiaris), cats (Felis catus), and Japanese weasels (Mustela itatsi). Roof rats showed different patterns of activity and behavior between the barns. However, because the pattern in both barns was nocturnal, the activity patterns of roof rats and carnivores showed a moderate to high degree of overlap. Therefore, roof rats did not appear to shift their activity patterns to avoid nocturnal carnivores. Taken together, the present study provides valuable information for rodent control on livestock farms in Japan.

A SAR Ship Detection Method Based on Adversarial Training.

SAR (synthetic aperture radar) ship detection is a hot topic due to the breadth of its application. However, limited by the volume of the SAR image, the generalization ability of the detector is low, which makes it difficult to adapt to new scenes. Although many data augmentation methods-for example, clipping, pasting, and mixing-are used, the accuracy is improved little. In order to solve this problem, the adversarial training is used for data generation in this paper. Perturbation is added to the SAR image to generate new samples for training, and it can make the detector learn more abundant features and promote the robustness of the detector. By separating batch normalization between clean samples and disturbed images, the performance degradation on clean samples is avoided. By simultaneously perturbing and selecting large losses of classification and location, it can keep the detector adaptable to more confrontational samples. The optimization efficiency and results are improved through K-step average perturbation and one-step gradient descent. The experiments on different detectors show that the proposed method achieves 8%, 10%, and 17% AP (Average Precision) improvement on the SSDD, SAR-Ship-Dataset, and AIR-SARShip, compared to the traditional data augmentation methods.

Ship Detection in Synthetic Aperture Radar Images under Complex Geographical Environments, Based on Deep Learning and Morphological Networks.

Synthetic Aperture Radar (SAR) ship detection is applicable to various scenarios, such as maritime monitoring and navigational aids. However, the detection process is often prone to errors due to interferences from complex environmental factors like speckle noise, coastlines, and islands, which may result in false positives or missed detections. This article introduces a ship detection method for SAR images, which employs deep learning and morphological networks. Initially, adaptive preprocessing is carried out by a morphological network to enhance the edge features of ships and suppress background noise, thereby increasing detection accuracy. Subsequently, a coordinate channel attention module is integrated into the feature extraction network to improve the spatial awareness of the network toward ships, thus reducing the incidence of missed detections. Finally, a four-layer bidirectional feature pyramid network is designed, incorporating large-scale feature maps to capture detailed characteristics of ships, to enhance the detection capabilities of the network in complex geographic environments. Experiments were conducted using the publicly available SAR Ship Detection Dataset (SSDD) and High-Resolution SAR Image Dataset (HRSID). Compared with the baseline model YOLOX, the proposed method increased the recall by 3.11% and 0.22% for the SSDD and HRSID, respectively. Additionally, the mean Average Precision (mAP) improved by 0.7% and 0.36%, reaching 98.47% and 91.71% on these datasets. These results demonstrate the outstanding detection performance of our method.

Helicobacter pylori East Asian type CagA hijacks more SHIP2 by its EPIYA-D motif to potentiate the oncogenicity.

CagA is a significant oncogenic factor injected into host cells by Helicobacter pylori, which is divided into two subtypes: East Asian type (CagAE), characterized by the EPIYA-D motif, and western type (CagAW), harboring the EPIYA-C motif. CagAE has been reported to have higher carcinogenicity than CagAW, although the underlying reason is not fully understood. SHIP2 is an intracellular phosphatase that can be recruited by CagA to perturb the homeostasis of intracellular signaling pathways. In this study, we found that SHIP2 contributes to the higher oncogenicity of CagAE. Co-Immunoprecipitation and Pull-down assays showed that CagAE bind more SHIP2 than CagAW. Immunofluorescence staining showed that a higher amount of SHIP2 recruited by CagAE to the plasma membrane catalyzes the conversion of PI(3,4,5)P3 into PI(3,4)P2. This alteration causes higher activation of Akt signaling, which results in enhanced IL-8 secretion, migration, and invasion of the infected cells. SPR analysis showed that this stronger interaction between CagAE and SHIP2 stems from the higher affinity between the EPIYA-D motif of CagAE and the SH2 domain of SHIP2. Structural analysis revealed the crucial role of the Phe residue at the Y + 5 position in EPIYA-D. After mutating Phe of CagAE into Asp (the corresponding residue in the EPIYA-C motif) or Ala, the activation of downstream Akt signaling was reduced and the malignant transformation of infected cells was alleviated. These findings revealed that CagAE hijacks SHIP2 through its EPIYA-D motif to enhance its carcinogenicity, which provides a better understanding of the higher oncogenic risk of H. pylori CagAE.

Ikaros sets the threshold for negative B-cell selection by regulation of the signaling strength of the AKT pathway.

Inhibitory phosphatases, such as the inositol-5-phosphatase SHIP1 could potentially contribute to B-cell acute lymphoblastic leukemia (B-ALL) by raising the threshold for activation of the autoimmunity checkpoint, allowing malignant cells with strong oncogenic B-cell receptor signaling to escape negative selection. Here, we show that SHIP1 is differentially expressed across B-ALL subtypes and that high versus low SHIP1 expression is associated with specific B-ALL subgroups. In particular, we found high SHIP1 expression in both, Philadelphia chromosome (Ph)-positive and ETV6-RUNX1-rearranged B-ALL cells. As demonstrated by targeted knockdown of SHIP1 by RNA interference, proliferation of B-ALL cells in vitro and their tumorigenic spread in vivo depended in part on SHIP1 expression. We investigated the regulation of SHIP1, as an important antagonist of the AKT signaling pathway, by the B-cell-specific transcription factor Ikaros. Targeted restoration of Ikaros and pharmacological inhibition of the antagonistic casein kinase 2, led to a strong reduction in SHIP1 expression and at the same time to a significant inhibition of AKT activation and cell growth. Importantly, the tumor suppressive function of Ikaros was enhanced by a SHIP1-dependent additive effect. Furthermore, our study shows that all three AKT isoforms contribute to the pro-mitogenic and anti-apoptotic signaling in B-ALL cells. Conversely, hyperactivation of a single AKT isoform is sufficient to induce negative selection by increased oxidative stress. In summary, our study demonstrates the regulatory function of Ikaros on SHIP1 expression in B-ALL and highlights the relevance of sustained SHIP1 expression to prevent cells with hyperactivated PI3K/AKT/mTOR signaling from undergoing negative selection.

A streamlined approach for intelligent ship object detection using EL-YOLO algorithm.

Maritime objects frequently exhibit low-quality and insufficient feature information, particularly in complex maritime environments characterized by challenges such as small objects, waves, and reflections. This situation poses significant challenges to the development of reliable object detection including the strategies of loss function and the feature understanding capabilities in common YOLOv8 (You Only Look Once) detectors. Furthermore, the widespread adoption and unmanned operation of intelligent ships have generated increasing demands on the computational efficiency and cost of object detection hardware, necessitating the development of more lightweight network architectures. This study proposes the EL-YOLO (Efficient Lightweight You Only Look Once) algorithm based on YOLOv8, designed specifically for intelligent ship object detection. EL-YOLO incorporates novel features, including adequate wise IoU (AWIoU) for improved bounding box regression, shortcut multi-fuse neck (SMFN) for a comprehensive analysis of features, and greedy-driven filter pruning (GDFP) to achieve a streamlined and lightweight network design. The findings of this study demonstrate notable advancements in both detection accuracy and lightweight characteristics across diverse maritime scenarios. EL-YOLO exhibits superior performance in intelligent ship object detection using RGB cameras, showcasing a significant improvement compared to standard YOLOv8 models.

Finer Particle Size Distribution and Potential Higher Toxicity of Elemental Carbon and Polycyclic Aromatic Hydrocarbons Emitted by Ships after Fuel Oil Quality Improvement.

Ship emissions are a significant source of air pollution, and the primary policy to control is fuel oil quality improvement. However, the impact of this policy on particle size distribution and composition characteristics remains unclear. Measurements were conducted on nine different vessels (ocean-going vessels, coastal cargo ships, and inland cargo ships) to determine the impact of fuel upgrading (S < 0.1% m/m marine gas oil (MGO) vs S < 0.5% m/m heavy fuel oil (HFO)) on elemental carbon (EC) and polycyclic aromatic hydrocarbons (PAHs) emitted by ships. (1) Fuel improvement significantly reduced EC and PAH emission, by 31 ± 25 and 45 ± 38%, respectively. However, particle size distributions showed a trend toward finer particles, with the peak size decreasing from DP = 0.38-0.60 µm (HFO) to DP = 0.15-0.25 µm (MGO), and the emission factor of DP < 100 nm increased. (2) Changes in emission characteristics led to an increase in the toxicity of ultrafine particulate matter. (3) Ship types and engine conditions affected the EC and PAH particle size distributions. Inland ships have a more concentrated particle size distribution. Higher loads result in higher emissions. (4) The composition and engine conditions of fuel oils jointly affected pollutant formation mechanisms. MGO and HFO exhibited opposite EC emissions when emitting the same level of PAHs.

Evaluating infectious disease outbreak potential and mitigation effectiveness on cruise ships.

The cruise ship sector is a major part of the tourism industry, and an estimated over 30 million passengers are transformed worldwide each year. Cruise ships bring diverse populations into proximity for many days, facilitating the transmission of respiratory illnesses. The objective of this study is to develop a modeling framework to inform the development of viable disease risk management policies and measures to control disease outbreaks on cruises. Our model, parameterized and calibrated using the data of the COVID-19 outbreak on the Diamond Princess cruise ship in 2020, is used to assess the impact of the mitigation measures such as mask wearing, vaccination, on-board and pre-traveling testing measures. Our results indicate mask wearing in public places as the cheapest and most affordable measure can drop the number of cumulative confirmed cases by almost 50%. This measure along with the vaccination by declining the number of the cumulative confirmed cases by more than 94% is the most effective measure to control outbreaks on cruises. According to our findings, outbreaks are more predominant in the passenger population than the crew members, however, the protection measures are more beneficial if they are applied by both crew members and passengers. Regarding the testing measure, pre-traveling testing is more functional than the on-board testing to control outbreaks on cruises.

A Compensation Method for the Geomagnetic Measurement Error of an Underwater Ship-Borne Magnetometer Based on Constrained Total Least Squares.

When magnetic matching aided navigation is applied to an underwater vehicle, the magnetometer must be installed inside the vehicle, considering the navigation safety and concealment of the underwater vehicle. Then, the interference magnetic field will seriously affect the accuracy of geomagnetic field measurement, which directly affects the accuracy of geomagnetic matching aided navigation. Therefore, improving the accuracy of geomagnetic measurements inside the vehicle through error compensation has become one of the most difficult problems that requires an urgent solution in geomagnetic matching aided navigation. In order to solve this problem, this paper establishes the calculation model of the internal magnetic field of the underwater vehicle and the geomagnetic measurement error model of the ship-borne magnetometer. Then, a compensation method for the geomagnetic measurement error of the ship-borne magnetometer, based on the constrained total least square method, is proposed. To verify the effectiveness of the method proposed in this paper, a simulation experiment of geomagnetic measurement and compensation of a ship-borne three-axis magnetometer was constructed. Among them, to be closer to the real situation, a combination of the geomagnetism model, the elliptic shell model and the magnetic dipole model was used to simulate the internal magnetic field of the underwater vehicle. The experimental results indicated that the root mean square error of geomagnetic measurement in an underwater vehicle was less than 5 nT after compensation, and the accuracy of geomagnetic measurement met the requirements of geomagnetic matching aided navigation.

Normative Values for Sternoclavicular Joint and Clavicle Anatomy Based on MR Imaging: A Comprehensive Analysis of 1591 Healthy Participants.

Background: The clavicle remains one of the most fractured bones in the human body, despite the fact that little is known about the MR imaging of it and the adjacent sternoclavicular joint. This study aims to establish standardized values for the diameters of the clavicle as well as the angles of the sternoclavicular joint using whole-body MRI scans of a large and healthy population and to examine further possible correlations between diameters and angles and influencing factors like BMI, weight, height, sex, and age. Methods: This study reviewed whole-body MRI scans from the Study of Health in Pomerania (SHIP), a German population-based cross-sectional study in Mecklenburg-Western Pomerania. Descriptive statistics, as well as median-based regression models, were used to evaluate the results. Results: We could establish reference values based on a shoulder-healthy population for each clavicle parameter. Substantial differences were found for sex. Small impacts were found for height, weight, and BMI. Less to no impact was found for age. Conclusions: This study provides valuable reference values for clavicle and sternoclavicular joint-related parameters and shows the effects of epidemiological features, laying the groundwork for future studies. Further research is mandatory to determine the clinical implications of these findings.

An improved sampled-data control for a nonlinear dynamic positioning ship with Takagi-Sugeno fuzzy model.

This article considered the sampled-data control issue for a dynamic positioning ship (DPS) with the Takagi-Sugeno (T-S) fuzzy model. By introducing new useful terms such as second-order term of time, an improved Lyapunov-Krasovskii function (LKF) was constructed. Additionally, the reciprocally convex method is introduced to bound the derivative of LKF. According to the constructed LKF, the sampling information during the whole sampling period was fully utilized, and less conservatism was obtained. Then, the stability condition, robust performance, mode uncertainty and sampled-data controller design were analyzed by means of the linear matrix inequality (LMI). Finally, an example was given to demonstrate the effectiveness of the proposed method.