Water is the most limited resource in the world and additional water resources must be found to provide clean and safe water for the growing population. Aim of this study is to analyze the ancient Ayurveda knowledge and to integrate with modern science to achieve global water need. Extensive literature survey was done on water based on ancient Ayurveda texts including Charaka Samhita, Susruta Samhita, Bhavaprakasha as well as published journal articles across the databases of google scholar, PubMed and Science Direct to justify the purpose of the study. Ayurveda classifies water as Divya jala (water from sky) and Bhauma jala (terrestrial water) and has extensively indicated their health benefits. Bhavaprakasha and Susruta samhita both have recommended specific types of water to be used for domestic purposes during specific seasons depending on their qualities and therapeutic actions to the body. Accordingly, lake and tank water is recommended during early winter, water from wells and waterfalls or springs during spring and summer seasons. All types of water are suitable to be consumed during autumn season due to natural purification by the Agastya nakshatra (star Canopus) because water collected during the Star Canopus scientifically proved of reduced levels of pH, total alkalinity, chlorides, CaCO3, biological oxygen demand and microbiological bacterial count. Ayurveda text also explains the suitable forms of water for consumption for adults and children, storage of water in different types of vessels and their health benefits. Therefore, the traditional knowledge of Ayurveda on water is valuable information at the present scientific context, to provide safe and clean water for the growing population for healthy living.

In the last few years, the increasing trend of vessel density, different types of vessels, and the increased need for real-time data have made maritime traffic management significantly more difficult. This study presents a situation-aware framework based on stacked ensemble learning and cloud-edge hybridization, which is aimed at enhancing the maritime traffic monitoring and control systems. This approach combines stacked ensemble learning with a meta-model for vessel type classification and employs the concept of cloud-edge architecture to strike a balance between computational efficiency and delay minimization. While the edge layer takes care of real-time inference and situational analysis on the go, the cloud layer takes care of model training and amalgamation of data from various sources. Our evaluation made use of a comprehensive maritime vessel dataset and compared the performance with the state-of-the-art deep learning models (VGG16, VGG19, DenseNet121, and ResNet50). Our experiments show that the stacked ensemble learning with a meta-model significantly outperforms the traditional ones, achieving an overall accuracy of 0.98, macro average precision of 0.97, macro average recall of 0.98, and an F1-score of 0.98. Both ROC and PR curves also demonstrate excellent AUC values, which tend to 1.00 for almost all categories of vessels, which is a strong performance in distinguishing vessels from each other. Test predictions are outstandingly accurate, with confidence in vessel classification exceeding 99% in most cases. From these results, the proposed method shows robustness, scalability, and effectiveness for real-time maritime surveillance, naval defense systems, and autonomous vessel traffic control in industrial IoT environments.

The purpose of this study was to analyze the vascular characteristics around pulmonary nodules with a diameter of 10-20mm. To explore their predictive value for the pathological nature of pulmonary nodules, computed tomography (CT) combined with three-dimensional computed tomography-bronchography and angiography (3D-CTBA) technique have been chosen as the method. 202 patients who were treated from January 2020 to December 2022 have been studied. They were all diagnosed with pulmonary ground glass nodules (GGNs) and obtained final pathologic results at the Department of Thoracic Surgery of Hebei General Hospital. In the meanwhile, the medical records were complete be enrolled in this study. All patients had underwent a chest CT scan in our hospital. The vessels surrounding the nodules were categorized into four types based on their relationships with the pulmonary nodules on the 3D reconstructed images. Type I (passing through): the vessel passes through the lesion, with no significant change in vessel size or shape throughout the path of travel; Type II (bypassed): vessels pass adjacent to the nodule, but no branch vessels supply the diseased tissue; Type III (twisted/dilated): twisting or thickening of blood vessels passing through the nodule, but no increase in number; Type IV (complex): a more complex vascular system other than those described in the above types, such as with irregular vasodilatation and convergent coexistence of vessels from multiple supply vessels. Data was analyzed by SPSS (version 26.0, Inc, Chicago, USA), with continuous data expressed as mean and standard deviation/Median (IQR), categorical variables expressed as numbers and percentages. For quantitative variables were analyzed using the Kruskal–Wallis test or Mann–Whitney U test. Categorical variables were analyzed using Fisher’s exact test or Chi-square test. Furthermore, the GGN-vessel relationship was compared between patients with microinvasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC). A P-value ≤ 0.05 was considered statistically significant. The Receiver Operating Characteristic (ROC) curve was used to determine the optimal diameter cut-off values for distinguishing IAC from MIA in nodules measuring 10–20 mm, with the Youden index applied to identify the cut-off value with the highest sensitivity and specificity. According to the pathology, all patients were divided into 3 groups:Benign nodule group (32 cases. 2 cases are sclerosing pneumoblastoma. 18 cases are found inflammatory lesions. 2 cases are pulmonary sarcoidosis. 1 case is pulmonary cyst. 7 cases are pulmonary hamartoma. 2 cases are pulmonary tuberculosis). Pre-infiltrative lesion group (36 cases. 4 cases are atypical adenomatous hyperplasia (AAH). 32 cases are adenocarcinoma in situ(AIS)). Infiltrative adenocarcinoma group (134 cases. 48 cases are MIA. 86 cases are IAC). The diameter of the pulmonary nodules of each group is: 13.0(11.0–14.7)mm, 13.0(12.0–15.0)mm, and 16.0(14.0–18.0)mm. The difference is statistically significant (P ≤ 0.001). 101 cases of mixed ground glass nodules (mGGNs) were found in all invasive adenocarcinoma groups and count a percentage of 75.4% (P ≤ 0.001). In invasive adenocarcinoma group, Type III (28/31) counts 90.3% (P = 0.009) and type IV (21/23) counts 91.3% (P = 0.008). This result is statistically different. The combination of type III and type IV vascular relationships showed a significant difference in the differential diagnostic value of the three groups of lung nodules (P < 0.001). For nodules 10–20 mm in diameter, the diameter cut-off value for IAC and MIA was 13.5 mm (AUC = 0.814, 95% CI: 0.74–0.89; specificity = 96.5%, sensitivity = 52.1%). The presence of Type IV vascular relationships in pure ground-glass nodules (pGGNs) showed a significant association with invasive adenocarcinoma (P = 0.038). Moreover, this vascular pattern demonstrated a significantly higher prevalence in invasive adenocarcinoma compared to pre-invasive lesions (P < 0.001). The different types of vessels ( arteries, veins, arteries and veins) crossing the nodule were analyzed showing no statistically significant difference (P = 0.078). For nodules with diameters of 10–20 mm, the presence of type III and IV vascular relationships in surrounding vasculature, a solid component, and a diameter ≥ 13.5 mm are more indicative of invasive lesions.

The article proposes an integrated model for assessing and adaptive management of the operational safety of a ship, which takes into account the temporal dynamics of risk, the technical condition of subsystems, the effectiveness of safety barriers and the impact of the external environment. The model is based on a probabilistic approach and includes generalized safety indicators: integrated risk index, safety margin, predicted time to critical condition, and system stability index. The developed algorithm provides for consistent monitoring of parameters, risk assessment in ship subsystems, modeling of barrier degradation, and dynamic aggregation of the overall risk. The verification was carried out on the basis of eight scenarios: from basic to stressful situations (power loss, combined impact, intensive maneuvering, etc.). The model features a combination of mathematical modeling and algorithmic response logic, including protection activation conditions and real-time adaptation of parameters. The use of time series and the LSTM model for risk prediction ensures proactive threat detection and decision-making before critical situations occur. The proposed system can be integrated into intelligent safety management platforms, in particular in autonomous or semi-autonomous shipping. The model has a modular structure, which ensures its flexibility and scalability for different types of vessels. Due to the use of weighting factors for the criticality of subsystems, it is possible to adapt the assessment logic to specific ship architectures and operating modes. Particular attention is paid to verifying the results based on simulated scenarios that demonstrate the dependence of the risk index and safety margin on the impact of certain factors, which allows not only post facto analysis but also the active development of protective strategies, which is relevant in the context of the transition to intelligent control systems in maritime transport. The practical value of the model lies in its ability not only to assess the current level of safety, but also to formulate recommendations for changing the speed, route, or mode of loading of the vessel. This approach ensures proactive safety management in conditions of high uncertainty and multifactorial threats

To address the challenge of detecting low-frequency electric field signals from vessels in complex marine environments, a vessel shaft frequency electric field feature extraction method based on intrinsic mode function energy arrangement entropy values is proposed, building upon a scaled model. This study initially establishes a measurement system for shaft frequency electric fields, utilizing a titanium-based oxide electrode to construct an equivalent dipole source simulating the shaft frequency electric field signals of different types of vessels. Subsequently, a comparative analysis of the time-domain and frequency-domain characteristics of signals after modal decomposition is conducted. A feature extraction method is then proposed that combines the maximum average energy of intrinsic mode functions with arrangement entropy values to achieve discrimination of target signals. Finally, the feasibility of the proposed method is validated through sea trials. The results indicate that the method can successfully screen different types of typical vessels and address the target screening failure caused by slight differences in the characteristic parameters of the shaft frequency electric field signal. The entropy difference has been improved from 0.05 to about 0.2, and the difference rate of the shaft frequency electric field signal has been improved by 75%. This has effectively reduced the false alarm rate of target detection.

Problem Statement and Purpose. The article substantiates the importance of water routes, which are crucial for maritime transport. Maritime transport plays a key role in world trade (80% of world goods are transported by sea). It is established that in today’s conditions the nature of international maritime transport and trade is changing under the influence of a number of trends. It is determined that the Integrated Maritime Policy (IMP) is designed to provide a more consistent approach to maritime issues based on increased coordination between different aspects of policy in this area. The opening of intercontinental sea routes has radically changed world trade relations. Data &amp; Methods. The article uses maps of world maritime transport, showing the density of maritime transport around the world and indicating global maritime routes. They were compiled using data from the International Monetary Fund (IMF) in partnership with the World Bank under the IMF’s World Maritime Trade Monitoring System. The data cover the period from January 2015 to February 2021 and include five different types of vessels: merchant vessels, fishing vessels, oil and gas vessels, passenger vessels and recreational vessels. Results. Maritime transport delivers all kinds of goods to other countries from different parts of the world, and these shipments are distinguished by safety and speed. In addition, from an economic point of view, it is the most efficient and cheapest way to transport goods. The nature of maritime transport and international maritime transport has been shaped for more than one year under the influence of a number of changes, ranging from containerization and trade liberalization to deregulation, the expansion of the private sector in the transport sector, as well as political and geopolitical shifts. The impact of some of these trends has been particularly strong in recent years. In addition, the evolution of the maritime transport industry has been driven by trends associated with the emergence of new major players in various sectors, primarily in developing countries. Understanding the key factors that determine the comparative or competitive advantages of a particular port or other maritime transport-related business can open up interesting growth opportunities in developing countries.

Abstract. The high-resolution ship emission inventory serves as a crucial dataset for various disciplines including atmospheric science, marine science, and environmental management. Here, we present a global high-spatiotemporal-resolution ship emission inventory at a resolution of 0.1° × 0.1° for the years 2013 and 2016–2021, generated by the state-of-the-art Shipping Emission Inventory Model (SEIMv2.2). Initially, the annual 30 billion Automatic Identification System (AIS) data underwent extensive cleaning to ensure data validity and accuracy in temporal and spatial distribution. Subsequently, integrating real-time vessel positions and speeds from AIS data with static technical parameters, emission factors, and other computational parameters, SEIM simulated ship emissions on a ship-by-ship, signal-by-signal basis. Finally, the results were aggregated and analyzed. In 2021, the ship activity dataset established based on AIS data covered 109 300 vessels globally (101 400 vessels reported by the United Nations Conference on Trade and Development). Concerning the major air pollutants and greenhouse gases, global ships emitted 847.2×106 t of CO2, 2.3×106 t of SO2, 16.1×106 t of NOx, 791.2 kt of CO, 737.3 kt of HC (hydrocarbon), 415.5 kt of primary PM2.5, 61.6 kt of BC (black carbon), 210.3 kt of CH4, and 45.1 kt of N2O in 2021, accounting for 3.2 % of SO2, 14.2 % of NOx, and 2.3 % of CO2 emissions from all global anthropogenic sources, based on the Community Emissions Data System (CEDS). Due to the implementation of fuel-switching policies, global ship emissions of SO2 and primary PM2.5 saw a significant reduction of 81.3 % and 76.5 % in 2021 compared to 2019, respectively. According to the inventory results, the composition of vessel types contributing to global ship emissions remained relatively stable through the years, with container ships consistently contributing ∼ 30 % of global ship emissions. Regarding vessel age distribution, the emission contribution of vessels built before 2000 (without Tier standards) has been declining, dropping to 10.2 % in 2021, suggesting that even a complete phase-out of these vessels would have limited potential for reducing NOx emissions in the short term. On the other hand, the emission contribution of vessels built after 2016 (meeting Tier III standard) kept increasing, reaching 13.3 % in 2021. Temporally, global ship emissions exhibited minimal daily fluctuations. Spatially, high-resolution emission characteristics of different vessel types were delineated. Patterns of ship emission contributions by different types of vessels vary among maritime regions, with container ships predominant in the North and South Pacific, bulk carriers predominant in the South Atlantic, and oil tankers prevalent in the Arabian Sea. The distribution characteristics of ship emissions and intensity also vary significantly across different maritime regions. Our dataset, which is accessible at https://doi.org/10.5281/zenodo.10869014 (Wen et al., 2024), provides a daily breakdown by vessel type and age; it is available for broad research purposes, and it will provide a solid data foundation for fine-scale scientific research and shipping emission mitigation.

The study examines the development of hybrid propulsion systems in the shipbuilding industry as an alternative to reduce polluting emissions. Through a systematic literature review of scientific sources between 2015 and 2024, technical configurations, emerging technologies and operational advantages were identified. The results show that the combination of thermal and electric motors with energy storage improves efficiency, reduces fuel consumption and minimizes CO₂, NOₓ and SOₓ emissions, especially in port maneuvers. In addition, these systems make it possible to comply with international regulations without altering the structure of the vessels. Advances in modular design, integration of renewable energies and their applicability in different types of vessels are highlighted. In conclusion, hybrid propulsion represents a viable intermediate solution towards a more sustainable, efficient and regulatory compatible maritime transport.

In the context of the deep-sea transportation supply chain, this paper addresses the complex decision-making problem of vessel allocation and carbon emission optimization for container shipping routes. A bi-level programming model is established, with the upper level aiming to minimize the total operational cost and the lower level focusing on minimizing carbon emissions. Using an example of an operator with five different types of vessels, a genetic algorithm is employed to determine the optimal vessel allocation scheme. The results indicate that the vessel allocation scheme obtained through multiple iterations of the model effectively reduces both carbon emissions and operational costs. Under the condition that the preset labor cost increases year by year, the use of model optimization can significantly reduce the growth of total operating costs. This paper provides theoretical support and practical guidance for shipping companies aiming to optimize decision-making in order to reduce operational costs and carbon emissions.

Maritime and inland ports are a cornerstone in the transport system of each state, providing different types of logistics services. In the last 80 years, they have undergone significant changes because of increased cargo volume transported by maritime and inland water transport, the transport capacity of different types of vessels, and their transport speed, and the introduction of push technology in inland navigation. Nowadays, they play a role as logistics hubs, and their activities can significantly impact the environment and local communities living close to these ports. The sustainability of ports is determined by some aspects like environmental, economic, social, and energy efficiency. This paper aims to investigate the sustainable port development. Nowadays port automatization is very important cognitive decision is needed to find the most suitable development strategies.

Warming weather has led to melting sea ice, and increasingly complex global geopolitics has drawn more countries’ attention to the Arctic. The Arctic Northeast Passage, as an emerging route connecting Eurasia, has seen a sharp increase in vessel activity. The period from 2015 to 2020, being a stable and undisturbed data period, is of significant theoretical importance for exploring the natural development of the Arctic Northeast Passage. The study found that the research period can be divided into three stages: from 2015 to 2017, the number of vessels grew slowly. In 2018 and 2019, the number of vessels and vessel activities saw significant growth, but an unexpected reverse growth occurred in 2020. Different types of vessels have unique activity characteristics and evolutionary patterns, influenced by the Arctic’s unique geographical environment, abundant natural resources, deepening Sino-Russian cooperation, and increasing global trade supply and demand. The results of this study aim to provide policymakers with analysis based on the initial development stage of the route, offering data support for future policy formulation, route planning, and research on the navigation safety of vessels on the Arctic Northeast Passage.

This study addresses marine pollution caused by debris entering the ocean through rivers. A physical and bubble barrier system has been developed to collect debris, but an effective identification and classification system for incoming vessels is needed. This study evaluates the effectiveness of deep learning models in identifying and classifying vessels in real time. The YOLO (You Only Look Once) v5 and v8 models are evaluated for vessel detection and classification. A dataset of 624 images representing 13 different types of vessels was created to train the models. The YOLOv8, featuring a new backbone network, outperformed the YOLOv5 model, achieving a high mean average precision (mAP@50) of 98.9% and an F1 score of 91.6%. However, YOLOv8’s GPU consumption increased by 116% compared to YOLOv5. The advantage of the proposed method is evident in the precision–confidence curve (PCC), where the accuracy peaks at 1.00 and 0.937 confidence, and in the achieved frames per second (fps) value of 84.7. These findings have significant implications for the development and deployment of real-time marine pollution control technologies. This study demonstrates that YOLOv8, with its advanced backbone network, significantly improves vessel detection and classification performance over YOLOv5, albeit with higher GPU consumption. The high accuracy and efficiency of YOLOv8 make it a promising candidate for integration into marine pollution control systems, enabling real-time identification and monitoring of vessels. This advancement is crucial for enhancing the effectiveness of debris collection systems and mitigating marine pollution, highlighting the potential for deep learning models to contribute to environmental preservation efforts.

The amount of knowledge we have aboutcaramoussals can be regarded as insufficient, as long as there is no agreement among scholars even on the naval architecture of these sailcrafts. As Ottoman sources have failed to advance research in this field, more attention should be paid to sources from Christian Europe. This paper investigates the existing sources, both Ottoman and Christian, and how historians have approached them so far, in order to assess if there is more to learn from them. Firstly, the paper concludes that there are two assumptions responsible for the inability of scholars to reach consensus: 1) that the term karamürsel is the name given to a single type of vessel; 2) that the available sources reflect contradictory and mutually exclusive information. Secondly, it refutes these presumptions as unfounded, and instead postulates as a methodological premise the following certainty: the information discussed here is complementary in nature, since it complements each other. The main argument employed here is provided by a Florentine manuscript containing drawings of vessels calledcaramussali, which have different characteristics. It can be inferred from this that the term karamürsel was not applied to a single type of vessel, but was used to designate simultaneously different types of vessels. This new approach to sources opens up several research opportunities, which the author insists on at the end of his paper.

Aims/Purpose: This cohort study investigates how the static and dynamic vessel parameters differ in patients with vascular occlusions and if there are any variations between the different types of vessel occlusion.Methods: Patients with the diagnosis of branch retinal artery obstruction BRAO, central retinal artery obstruction CRAO, branch retinal vein obstruction BRVO, central retinal vein obstruction CRVO, anterior ischaemic optic neuropathy AION and any combination of these Combo were included in our study. During their inpatient stay for cardiovascular diagnostics all patients were examined via static and dynamic vessel analysis (IMEDOS Health GmbH, Jena) to obtain the arteriovenous ratio (AVR), the central retinal artery equivalent (CRAE), the central retinal vein equivalent (CRVE) and the flicker light induced dilation of the retinal arteries (FLA) and veins (FLV). The vessel density was determined via OCT angiography (Heidelberg Engineering GmbH, Heidelberg).Results: 34 patients (70% male; 30% female; mean age 69.6 ± 11.3 years) with 9 (26.5%) CRVO, 8 (23.5%) AION, 5 (14.7%) BRAO and 4 (11.8%) CRAO, BRVO and Combo each have been included. The best visual acuity (BCVA) of the diseased eye differed significantly between the groups (AION 0.41 ± 0.61, CRAO 2.40 ± 0.20, BRAO 0.16 ± 0.05, CRVO 0.81 ± 0.86, BRVO 0.18 ± 0.10, Combo 1.53 ± 0.68; p &lt; 0.001) whereas the BCVA of the healthy eyes were comparable. The CRVE showed a significant difference between the groups CRAO – CRVO (p = 0.008), CRAO – Combo (p &lt; 0.001) and BRAO–Combo (p = 0.026). The FLV of the diseased eye was significantly higher in patients with AION than in the CRAO group (p = 0.049). The parameters of the static vessel analysis of the healthy eyes were comparable.Conclusions: Preliminary analysis shows significant differences of individual parameters of the retinal vessel analysis between different types of vascular occlusion. The diagnostic and research benefit for the individual patient remains to be further evaluated.

This study examines the operating expenses (OPEX) incurred by tankers and bulk carriers (bulkers), which are widely utilized vessels within the maritime sector, over the period from 2018 to 2021. The study specifically examines the movement of OPEX during the specified period and assesses the impact of the COVID-19 pandemic, vessel age, and size on OPEX. The study and comprehension of the behaviour of operating expenses hold significant importance for managerial purposes in shipping companies, as it is one of the two most critical expenses. The OPEX data is obtained from the Moore Maritime Index (MMI) database. The vessels are categorized into clusters based on age and size to facilitate a more focused and comprehensive analysis of OPEX. Data are examined through statistical analysis. The study’s findings suggest a positive correlation between OPEX and the age of vessels, with a stronger correlation observed in larger vessels. Furthermore, OPEX experienced a consistent increase throughout the entire study period, with the rate of increase directly linked to the vessel’s size. Ultimately, OPEX remained unaffected by the pandemic. This study is distinctive in that it examines not only the OPEX behaviour of different types of vessels but also considers vessel size subcategories and the impact of COVID-19.

When the abdominal cavity is inflamed, the lymphatic channel, i.e. the system of lymph vessels and lymph nodes, which play an important role in protecting the body from infections and ensuring proper metabolism, may be affected. Inflammation of the lymphatic channel can lead to its enlargement, soreness and even the formation of purulent foci. The study of morphometric changes in the lymphatic channel during inflammation is an important task for a more detailed understanding of inflammatory processes occurring in the body. The results of our experiments showed that during inflammatory processes of the abdominal cavity there are changes in the blood and lymphatic vessels. Relative volumes of vessels of different types per unit significantly decreased compared to the indicators of the control group. In submucosa and muscularis the morphometry data confirm the fact of arterial vessels spasm, lymphatic vessel reduction and simultaneous persistent venous vessels expansion. In rats with the developed inflammatory process in lymphatic vessels the expansion of lymphatic slits and significant thickening of the wall of large lymphatic vessels was found, which is associated with an increase in the volume of blood plasma, increasing the load on lymphatic vessels as a drainage system of the body.

Abstract Bronze vessels as grave goods in the burial of the Vicus at Biatorbágy. In the eastern area of Biatorbágy, north of the Autostrada / No 100, around the tenth kilometer stone in 1991, 1995, 2004 heading the planned construction works rescue excavations were carried out. Here several Roman bronze vessels, probably grave goods, were excavated. This necropolis belonged to the Roman vicus at Biatorbágy. Different types of vessels, a wine jug with a round opening, a handled dish for hand washing, on which are a depiction of a sleeping African and Bacchic attributes, a water jug with a spout, some fragments belonging to bucket or cauldron, a second water jug with sharp projecting shoulder, a jug with round mouth and decorated handle, a bath-saucer came to light. The first jug – compiled from old parts – a strange creation by a local master, was originally produced at the end of the 1st century A.D. The handled dish and the bath-saucer were fabricated at the end of the 1st century A.D.–beginning of the 2nd century A.D. Both water jugs were made around the middle of the 2nd century A.D.

Abstract Introduction/Objective Sclerosing angiomatoid nodular transformation (SANT) is a benign vascular lesion of spleen with uncertain etiology. It predominately affects women between 30 to 60 years of age. Clinically, it is asymptomatic or can cause abdominal pain, but usually discovered incidentally on imaging, which can identify a mass but may not provide a definitive diagnosis. In uncertain vascular lesions, there is always a risk of spontaneous rupture of large vessels and potential for spreading malignancy. Hence, the final diagnosis is rendered on microscopy after splenectomy Methods/Case Report A middle age female came to clinic complaining of abdominal pain. Radiology showed solid splenic mass and the patient underwent splenectomy. Gross examination showed a 3 cm white firm mass with focal hemorrhage. Microscopy revealed multiple nodules of variable sizes surrounded by fibrosclerotic stroma. The nodules showed round to slit-like vascular spaces with numerous red blood cells. The internodular stroma consisted of dense fibrous tissue with scattered plump myofibroblasts and lymphoplasmacytic cells. These distinctive features lead to the diagnosis of SANT Results (if a Case Study enter NA) SANT possessed characteristic histologic features with distinctive immunohistochemistry (IHC). IHC reveals three different types of vessels within the nodules: 1. Small veins (CD34- CD31+ CD8-); 2. Sinusoids (CD34- CD31+ CD8+); and 3. Capillaries (CD34+ CD31+ CD8-). All 3 vessels are negative for CD21/CD35 and CD68. Hemangioma and littoral cell angioma are two frequent vascular tumors in the spleen that should be differentiated. Both lesions lack microscopic features of SANT and have only a single type of vessels. The vessels in hemangioma are (CD31+ CD34+ CD8-); while in littoral cell angioma they are (CD31+ CD34- CD8- CD21+ CD68+) Conclusion There are no specific clinical or radiologic findings for SANT. It is important to recognize these characteristic features and to differentiate it from other benign and malignant lesions, such as angiosarcoma. A thorough histopathologic examination and IHC are helpful to make the correct diagnosis

Vessels frequently encounter challenging marine conditions that expose the propeller-hull to corrosive water and marine fouling. These challenges necessitate innovative approaches to optimize propeller-hull performance. This study aims to assess a method for predicting propeller-hull degradation. The proposed solution revolves around an innovative Key Performance Indicator (KPI) based on Artificial Neural Networks (ANNs). Our objective is to validate the findings; thus, a thorough comparison is conducted between the proposed method and the baseline solution derived from the ISO-19030. Emphasis is placed on determining the optimal parameters for computing the KPI, which involves applying various features, filters, and pre-processing techniques. The proposed method is tested on real data collected by an Internet of Things (IoT) system installed in different types of vessels. Four distinct experiments with ANNs are conducted. Results demonstrate that the ANN-based indicator offers greater accuracy in predicting propeller-hull degradation compared to the baseline method. Additionally, it is demonstrated that selecting a diverse set of features and implementing consistent filtering and preprocessing techniques enhance the performance of the traditional indicator. The utilization of Deep Learning (DL) in the maritime industry is of great significance, as it enables a comprehensive and dynamic assessment of predictive maintenance of the propeller-hull. The DL index method holds potential for diverse maintenance applications, providing a holistic platform with anticipated environmental and financial benefits.

Black carbon (BC) has a significant impact on air quality, climate change, and human health. Studies on BC from vessel exhaust have been focused on in recent years. To realize the contribution of BC from vessels to ambient air quality, 28 months of BC variation were observed from February 2019 to May 2022, including 3 fishing moratoriums and 2 normal periods. The results showed that the average daily concentration of BC in the fishing moratorium was significantly lower than that in the normal period. The difference proportion of the BC concentration between 370 and 880 nm was calculated over the whole period. As a result, the mean difference value in the fishing moratorium from February to May was 0.06 ± 0.07, and the normal period was -0.02 ± 0.05. The aethalometer model indicated that BC was greatly affected by fossil fuel combustion in the normal period. The effect of vessel emissions on regional BC concentrations was considerable. In addition, 16 PAHs and 21 elements in PM emitted from 24 vessels of different types were sampled and analyzed in Dianshan Lake and the Taipu River. EC accounted for the highest proportion (23.64%) in the sample of small trawlers compared to the emissions from cargo ships with large tonnages. The component profiles of vessel exhaust showed that Zn, As, phenanthrene (Phe), anthracene (Ant), fluoranthene (Fla), and pyrene (Pyr) were the dominant species, although some of these species were mainly recognized as characteristic factors of coal combustion. To improve the accuracy of identifying the vessel source, the diagnostic ratios of Ant/(Ant + Phe), BaA/(BaA + Chr), Phe/Ant, and BaA/Chr were provided, and they exhibited the obvious characteristics of fuel combustion.