Field Of Marine Science Research Articles

Application of species distribution models in predicting the distribution of marine macrobenthos.

Species distribution models (SDMs) are valuable tools in predicting species distribution ranges and the suitable habitats, which are based on environmental conditions and species distribution data. These models encompass correlative models, mechanistic models, and mechanistic-correlative models. In the field of marine science, SDMs have been extensively used for predicting the spatial distribution patterns of various marine organisms including fish, mammals, algae, et al. However, the application of SDMs in predicting the distribution of macrobenthos remains scarce. Understanding the distribution of macrobenthos, the integral components of marine ecosystems, has significant implications for ecological conservation and resource management. We reviewed common methodologies employed in SDMs and presented case studies using different models to predict the distribution patterns of marine macrobenthos. Further, we emphasized the use of correlative and mechanistic models to analyze the impact of climate change on the spatial distribution of marine macrobenthos. Finally, we discussed the challenges and prospects associated with SDMs. With the advances in remote sensing technology and modeling techniques, SDMs are becoming increasingly pivotal in marine ecological research, which could offer a robust scientific foundation for addressing climate change and preserving marine biodiversity.

Time series (2003–15) analysis of selected physicochemical parameters in Indian Ocean: Cumulative impacts prediction on coral bleaching using machine learning

Coral bleaching is an important ecological threat worldwide, as the coral ecosystem supports a rich marine biodiversity to survive. Sea surface temperature was considered a major culprit; however, later it was observed that other water parameters like pH, tCO2, fCO2, salinity, dissolved oxygen, etc. also play a significant role in bleaching. In the present study, all these parameters of the Indian Ocean area for 15 years (2003–2017) were collected and analysed using machine learning language. The main aim is to see the cumulative impacts of various ocean parameters on coral bleaching. Introducing machine learning in environmental impact assessment studies is a new approach, and the prediction of coral bleaching using simulation of physico-chemical parameters interactions shows 94.4 % accuracy for the prediction of the future bleaching event. This study can be probably the first step in the application of the machine learning language for the prediction of coral bleaching in the field of marine science.

Water education, ocean literacy and arts integration

Life below water, as well as at waters, is threatened due to human activity that has caused global warming. As UNESCO stresses, “the time to learn and act for our planet is now”. This article reports on a literature review of existing action research on water education, ocean literacy and arts integration carried out by an emerging interdisciplinary research group stretching across the fields of marine science, arts and science education. Joined by a concern for water education and ocean literacy, the interdisciplinary teams screened 368 research articles with the assistance of the program Covidence which helps with streamlining the literature review processes in a team. Ending up with 14 relevant articles that were analysed in-depth, the authors argue that arts-integrated water education projects take place locally across higher education, formal education and informal education in collaborative teams. The action research projects screened promote and develop open-ended, inquiry-based and creative pedagogies, seeking to foster the capacity to act for sustainable living in a more-than-human world. However, all research projects screened in the review seemed to develop water education more generally. There is a lack of literature researching how the connection to ocean literacy including life below water specifically, can be nurtured.

Potential Applications of Whisker Sensors in Marine Science and Engineering: A Review

Perception plays a pivotal role in both biological and technological interactions with the environment. Recent advancements in whisker sensors, drawing inspiration from nature’s tactile systems, have ushered in a new era of versatile and highly sensitive sensing technology. Whisker sensors, which mimic the tactile hairs of mammals, offer both high sensitivity and multifunctionality. They excel in capturing fine-grained environmental data, detecting various stimuli with precision, and finding applications in diverse domains. This review explores the integration of whisker sensors in potential marine applications. Categorized into six types, these sensors are invaluable for tasks such as marine structure monitoring, measurement instruments, tactile perception in marine robots, and non-contact sensing in the marine environment. Challenges and potential solutions are examined, along with the prospects of whisker sensors in the field of marine science and engineering. In an era that demands adaptable sensing solutions, whisker sensors emerge as pivotal components, enabling machines and devices to perceive and respond to external stimuli with heightened sensitivity and versatility. Their application in the marine domain holds substantial promise, propelling advancements in the realms of marine science and engineering.

Evaluation of Electromagnetic Fields of Extremely Low-Frequency Horizontal Electric Dipoles at Sea–Air Boundaries

The technologies of undersea detection and communication, seabed sensor networks, and geophysical detection using electromagnetic waves have emerged as research focal points within the field of marine science and engineering. However, most studies have focused on the propagation of electromagnetic fields over long distances within the shallow “sea-seabed” environment. This paper introduces a quasi-static approximation method to address the Sommerfeld numerical integration challenge within the near-field region, employing the horizontal electric dipole (HED) as a model. It derives the Sommerfeld numerical integral expressions under conditions where the wave-number ratio at the “seawater-air” boundary does not adhere to the requirement of |k0/k1| << 1 (where subscripts 0 and 1 denote seawater and air media, respectively). Building upon this, the paper simplifies the Bessel-Fourier infinite integral term within the integral expression to obtain Sommerfeld numerical integral approximations for the propagation of electromagnetic fields in the near region of extremely low frequency (ELF) within seawater. The study further conducts simulations and calculations to determine amplitude variations in electromagnetic field intensity generated by an ELF HED at different frequencies, dipole heights, and observation point depths. It concludes with an analysis of electromagnetic field propagation characteristics at the seawater-air boundary. Experimental findings highlight the lateral wave as the primary mode of electromagnetic wave propagation at this interface.

Hybrid quantum-classical convolutional neural network for phytoplankton classification

The taxonomic composition and abundance of phytoplankton have a direct impact on marine ecosystem dynamics and global environment change. Phytoplankton classification is crucial for phytoplankton analysis, but it is challenging due to their large quantity and small size. Machine learning is the primary method for automatically performing phytoplankton image classification. As large-scale research on marine phytoplankton generates overwhelming amounts of data, more powerful computational resources are required for the success of machine learning methods. Recently, quantum machine learning has emerged as a potential solution for large-scale data processing by harnessing the exponentially computational power of quantum computers. Here, for the first time, we demonstrate the feasibility of using quantum deep neural networks for phytoplankton classification. Hybrid quantum-classical convolutional and residual neural networks are developed based on the classical architectures. These models strike a balance between the limited function of current quantum devices and the large size of phytoplankton images, making it possible to perform phytoplankton classification on near-term quantum computers. Our quantum models demonstrate superior performance compared to their classical counterparts, exhibiting faster convergence, higher classification accuracy and lower accuracy fluctuation. The present quantum models are versatile and can be applied to various tasks of image classification in the field of marine science.

Microbe, climate change and marine environment: Linking trends and research hotspots

Microbes, or microorganisms, have been the foundation of the biosphere for over 3 billion years and have played an essential role in shaping our planet. The available knowledge on the topic of microbes associated with climate change has the potential to reshape upcoming research trends globally. As climate change impacts the ocean or marine ecosystem, the responses of these “unseen life” will heavily influence the achievement of a sustainable evolutionary environment. The present study aims to identify microbial-related research under changing climate within the marine environment through the mapping of visualized graphs of the available literature. We used scientometric methods to retrieve documents from the Web of Science platform in the Core Collection (WOSCC) database, analyzing a total of 2767 documents based on scientometric indicators. Our findings show that this research area is growing exponentially, with the most influential keywords being “microbial diversity,” “bacteria,” and “ocean acidification,” and the most cited being “microorganism” and “diversity.” The identification of influential clusters in the field of marine science provides insight into the hot spots and frontiers of research in this area. Prominent clusters include “coral microbiome,” “hypoxic zone,” “novel Thermoplasmatota clade,” “marine dinoflagellate bloom,” and “human health.” Analyzing emerging trends and transformative changes in this field can inform the creation of special issues or research topics in selected journals, thus increasing visibility and engagement among the scientific community.

Research on Seabed Sediment Classification Based on the MSC-Transformer and Sub-Bottom Profiler

This paper proposed an MSC-Transformer model based on the Transformer’s neural network, which was applied to seabed sediment classification. The data came from about 2900 km2 of seabed area on the northern slope of the South China Sea. Using the submarine backscattering intensity and depth data obtained by the sub-bottom profiler, combined with latitude and longitude information, a seabed dataset of the slope area of the South China Sea was constructed. Moreover, using the MSC-Transformer, the accurate identification and judgment of sediment types such as calcareous bio-silt, calcareous bio-clay silt, silty sand, medium sand and gravel sand were realized. Compared with the conventional deep neural network CNN, RNN, etc., the model shows advantages when applied to the sediment dataset of the shallow sea slope region of the South China Sea. This confirms the feasibility and validity of the model and provides a reliable and accurate tool for seabed sediment classification in the field of marine science. The completeness and accuracy of the dataset and the good performance of the model provide a solid foundation for the scientificalness and practicability of the study.

Cross domain fusion for spatiotemporal applications: taking interdisciplinary, holistic research to the next level

Exploiting the power of collective use of complementing data sources for the discovery of new correlations and findings offers enormous additional value compared to the summed values of isolated analysis of the individual information sources. In this article, we will introduce the concept of “cross domain fusion” (CDF) as a machine learning and pattern mining driven and multi-disciplinary research approach for fusing data and knowledge from a variety of sources enabling the discovery of answers of the question to be examined from a more complete picture. The article will give a basic introduction in this emerging field and will highlight examples of basic CDF tasks in the field of marine science.

Analysis of Chinese Machine Translation Training Based on Deep Learning Technology.

With the advent of the information age, people can establish good communication through Internet technology. Mechanical translation has become a key means to solve people's communication problems. However, there are still obstacles to communication between different languages. In order to solve this problem, this paper uses existing neural network technology to the English-Chinese bidirectional machine translation model in the field of marine science and technology. Based on deep learning technology, we collect Chinese and English abstracts and partial full texts of Chinese and English papers with marine science and technology as the key words and build a professional corpus in English and Chinese about marine science and technology. In the Chinese-English bidirectional translation model, the local weight sharing is introduced into the Chinese encoder and the English encoder, and the output of the Chinese encoder sublayer and the English encoder sublayer is fused as the output of the respective encoders, and the performance of the translation model is evaluated using the BLEU parameters. Through the training of the translation model, compared with the transformer model, the BLEU value of the model with local weight sharing and encoder sublayer fusion output is improved by 1.6 and 3.8 in the Chinese-English and English-Chinese translation directions, respectively. The PPL values in the Chinese-English and English-Chinese translation directions decreased by 18.72% and 14.62%, respectively. We demonstrate the effectiveness of the language translation model. Experiments show that the research on machine language adaptive technology based on deep learning can more smoothly realize the two-way translation of literature in the field of marine science and technology. Compared with traditional mechanical translation, this paper proposes a translation model based on the deep neural algorithm, which improves the effect of model training by constructing a Chinese-English corpus with the theme of marine science and technology.

The Hellenic Marine Observing, Forecasting and Technology System—An Integrated Infrastructure for Marine Research

Research infrastructures have been established throughout Europe in order to create robust organizations that will facilitate and enhance research and innovation processes and will advance society with innovative products and services. The Hellenic Integrated Marine Observing, Forecasting and Technology System (component of HIMIOFoTS RI) has been implemented in the framework of the National Roadmap for Research Infrastructures to form a large-scale infrastructure for the marine environment in Greece. It links together ocean observing and forecasting systems, coastal zone monitoring and management practices, as well as ocean engineering testing facilities. The overarching framework of the system supports the coordination of five organizations with expertise in the field of marine science and technology, the central management of research activities, and the common development of services and products. It comprises facilities and resources while it provides open access to research communities (academia, industry) to support the scientific advancements and innovation in their fields. The Hellenic Marine Observing, Forecasting and Technology System was further enhanced during its implementation through significant upgrades and developments in order to extend its observing capacity and the forecasting and technological abilities, while advancing the provided services and products.

Predictive capacity of Ecopath with Ecosim: Model performance and ecological indicators’ response to imprecision

The ecosystem modelling complex ‘Ecopath with Ecosim’ has been implemented extensively in the field of marine science; however, despite its widespread application, descriptions of its functionality remain arcane in the literature. This study conducts an evaluation of the software's prediction capacity using eight published Ecopath models. Response of six ecosystem-status indicators to four basic input variables to which imprecision had been added was investigated. Kempton's Q Index and total system throughput emerged as the most consistently responsive parameters. Moreover, input biomass was identified as a ‘high-leverage’ parameter, its influence on outputs being greater than that exerted by any other input variable. This study constitutes one of the first comprehensive investigations of the response of selected outputs to imprecise input values, and provides sufficient basis to warrant a sensitivity assessment of the software, as well as introduction of a dedicated tool to perform such a task within Ecopath with Ecosim.

Molecular and Cellular Mechanisms of Lipid Droplet Breakdown in the Liver of Chinese Soft-Shelled Turtle (Pelodiscus sinensis)

The Chinese soft-shelled turtle (Pelodiscus sinensis) is among the most primitive amphibians and reptiles in nature. On account of its environmental suitability and unique hibernation habit, the peculiar physiological phenomenon in P. sinensis attracted the attention of researchers in the field of marine science. The present study aimed to investigate the molecular mechanisms underlying the periodic variation of lipid droplet (LD) in the liver of P. sinensis. Histological results indicated that accumulated LD in the liver of P. sinensis during non-hibernation was gradually consumed during hibernation. RNA-Seq results revealed that genes responsible for carbohydrate catabolism were down-regulated during hibernation, while genes involved in lipid oxidation were up-regulated. These results suggest that energy metabolism in the liver of P. sinensis changes during hibernation, i.e., the energy generation mode shifted from carbohydrate catabolism to lipid oxidation. Further analysis of RNA-Seq results indicated that both lipolysis and autophagy could promote the degradation of hepatic LD during hibernation. To further determine the relationship between lipolysis and autophagy in the process of LD breakdown, we applied the inhibitors of lipolysis and autophagy (diethylumbelliferyl phosphate and 3-Methyladenine) in cultured primary hepatocytes of P. sinensis. The results indicated that lipolysis is the main way for LD degradation in the hepatocyte of P. sinensis. These data provide clear evidence about the seasonal changes in hepatocytes, corresponding with the different energy generation mode in the liver of P. sinensis.

Research on Semantic Analysis of Ocean Flow Field Based on Feature Similarity Collaborative Filtering

Ding, L., 2020. Research on semantic analysis of ocean flow field based on feature similarity collaborative filtering. In: Yang, D.F. and Wang, H. (eds.), Recent Advances in Marine Geology and Environmental Oceanography. Journal of Coastal Research, Special Issue No. 108, pp. 53–57. Coconut Creek (Florida), ISSN 0749-0208.In the field of ocean space-time data modeling research, fields and features are usually used to describe and organize ocean current phenomena, which is continuous and dynamic. However, during the process, the semantic analysis and expression of ocean flow field phenomena often produce heterogeneous problems. Because the feature theory can construct and define the conceptual model framework of domain knowledge system, the semantic analysis of the ocean flow field using the feature theory can be regarded as one of the ways to achieve the semantic sharing of the ocean flow field. Moreover, based on the collection and analysis of existing knowledge and concepts in the field of marine science, a quadruple-based organization in the paper is proposed for ocean flow field features, in which by expanding the concept features, attribute features, and relationship features, a basic structure-based approach is formed. The overall characteristic structure of the ocean flow field comprises concept, attribute, and relationship structure. Additionally, collaborative filtering is performed on the similarity of the structural features in the ocean flow field, and a formal grammatical expression system conforming to the characteristics of continuous flow field is also made so that a normative framework system is provided for the ocean flow field knowledge organization. The OWL language is used, which allows for the conduction of the formal expression and description of geostrophic flow, which provides semantic support for the integration and management of spatiotemporal data in the ocean flow fields.

Advancing Through the Pandemic From the Perspective of Marine Graduate Researchers: Challenges, Solutions, and Opportunities

The ongoing COVID-19 pandemic has brought about a new social and academic reality to researchers worldwide. The field of marine science, our own topic of interest, was also impacted in multiple ways, from cancellation of lab and field activities to postponement of onboard research. As doctoral/Ph.D. researchers, we have a time-sensitive academic path, and our current situation may constrain our academic future. At the same time, the pandemic demands revised strategies to deal with the ongoing difficulties and tackle similar future situations. In this perspective, we have gathered information on the challenges, solutions and opportunities for graduate researchers in the field of marine science by 1) discussing the relevant short and long term challenges caused by the pandemic, 2) providing feasible immediate and near-future solutions, 3) compiling opportunities (courses, scientific events, academic positions) and 4) creating a shared social media platform to make the available information on new opportunities more accessible. With this, we hope to add to the efforts to advance the academic career of marine graduates during this harsh period.

Foreword

Their great diversity and potential makes the development of marine and fisheries resources a priority for Indonesia, and a key orientation of long-term development planning. The development of valuable maritime and fishery resources has been encouraged by the government as a means to achieve food security and economic independence.These abundant marine and fisheries resources are the mainstay of Indonesia’s efforts to achieve Sustainable Development Goal (SDG) number 14: “Conserve and sustainably use the oceans, seas and marine resources for sustainable development”.As a contribution towards achieving SDG 14, each year the Faculty of Marine Science and Fisheries at Universitas Hasanuddin holds a national and international marine and fisheries symposium. The theme of the 2019 Symposium is “Managing Aquatic Resources for Sustainable Development”. This symposium will provide opportunities for participants to exchange information, knowledge and experience, as well as to initiate partnerships for research and outreach activities in the field of marine science and fisheries. These exchanges and partnerships will contribute towards enhancing the potential, development and utilization of marine and fishery resources in sustainable ways.A warm welcome to the researchers and participants from a wide cross-section of the marine and fisheries sector and thank you for coming to taking part in this Universitas Hasanuddin Marine Science and Fisheries Symposium. A heartfelt thank you is also due to the many people and organisations that have provided support or contributed in any way towards the successful realisation of this Symposium. May all the contributions made during this event provide valuable input to support marine and fisheries development in Indonesia.Makassar, 5 September 2019Dean,Dr.Ir.St Aisjah Farhum, MSi

Exploring the Potential Bioactive Properties of Marine Natural Products

Background: The marine world is highly diversified and is of great importance for its sustainable utilization as resources for human life. Although marine resource economy has its own share in the international market, there is only a handful of research occurring, the probability of not being completely explored. It has now become virtually possible to characterize the metabolites by bioanalytical techniques and molecular biology methodology. The marine biomes harbour largely diatoms, bacteria, fungi, actinomycetes, and cyanobacteria that are harnessed for their rich structurally diverse secondary metabolites. Recently, sponges and algae have gained commercial importance. Focuses are now shifting towards ascomycota, actinobacteria, cyanobacteria and other classes due to the advanced technology in extraction of the active components from these phyla and deeper exploration of the oceans. Method: A vast report on marine natural products have been regularly updated and published which clearly indicates the immense research undertaken in ocean flora and fauna. Herein we focus on providing information on the potential uses of marine natural products as bioactive agents in the last five years. The data were collected from research and review articles from peer-reviewed journals. Results: A total of 128 articles are citied in the present review covering broad spectrum of marine organism and their importance in the field of medical biology. Information on source organisms, collection sites, was assembled and a total of 120 biologically important compounds are described here. Conclusion: The present study provides information to bioprospecting efforts in the field of marine science. Herein, I have emphasized diverse biological activities isolated from marine microbes to invertebrate against many pathogenic microorganisms and cancer, thus making marine-derived natural products valuable commodities. Future prospects for the discovery of new marine products and process development could add to the economic importance.

Application of Discrete Element Method in the Field of Marine Science and Technology

Application of Discrete Element Method in the Field of Marine Science and Technology

The 2nd International Conference on Applied Marine Science and Fisheries Technology (MSFT) 2017

Welcome,Dear colleaguesOn behalf of organizing committee of International Conference and Tual State Fisheries Polytechnic, we are happy and great pleasure to extent a warm welcome to the 2017 MSFT The 2nd International Conference in Langgur, Southeast Maluku, Indonesia.This international conference was initiated to serve as a platform for the information exchange of research results from various fields of marine science and fisheries technology, including relevant socioeconomic and business disciplines, as well as the sharing of practical experiences in the utilization and management of the marine resources of small islands on an on-going basis. We also hope that the forum will promote the sharing of practical experiences in the utilization and management of the marine resources of small islands on an on-going basis.With over 50 National and International speakers from, Australia, France, US, Netherland, New Zealand and Indonesia, the conference play its role as bridge for small islands like Southeast Maluku and Maluku in particular and Indonesia in general to the world in order to accelerating sustainable development with applied marine science and fisheries technology.

Questions as indicators of ocean literacy: students’ online asynchronous discussion with a marine scientist

ABSTRACTIn this article, 61 high-school students learned about ocean acidification through a virtual laboratory followed by a virtual lecture and an asynchronous discussion with a marine scientist on an online platform: VoiceThread. This study focuses on the students’ development of ocean literacy when prompted to ask questions to the scientist. The students’ questions were thematically analysed to assess (1) the kind of reasoning that can be discerned as premises of the students’ questions and (2) what possibilities for enhancing ocean literacy emerge in this instructional activity. The results show how interacting with a scientist gives the students an entry point to the world of natural sciences with its complexity, uncertainty and choices that go beyond the idealised form in which natural sciences often are presented in school. This activity offers an affordable way of bringing marine science to school by providing extensive expertise from a marine scientist. Students get a chance to mobilise their pre-existing knowledge in the field of marine science. The holistic expertise of the marine scientist allows students to explore and reason around a very wide range of ideas and aspect of natural sciences that goes beyond the range offered by the school settings.