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New Techniques and Equipment in Large Offshore Aquaculture Platform
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New Techniques and Equipment in Large Offshore Aquaculture Platform

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Marine Aquaculture".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 14363

Special Issue Editors

Dr. Zhenhua Ma

E-Mail Website
Guest Editor
Sanya Fisheries Research Institute, South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Beijing, China
Interests: aquaculture; artificial breeding; larval fish; fish and shellfish physiology; fish behavior; diseases control; nutrition; recirculation system; pond culture; offshore sea cage
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jianguang Qin

E-Mail Website
Guest Editor
College of Science and Engineering, Flinders University, Adelaide, Australia
Interests: aquaculture nutrition; biotechnology; water quality; algal culture; fish; mollusks; crustaceans
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The far-reaching aquaculture model is one of the important areas for the expansion of the new space of aquaculture. The emergence of new technology and new equipment has promoted the rapid development of aquaculture worldwide. This Special Issue introduces the latest progress in far-reaching marine aquaculture technology and equipment from the aspects of far-reaching marine aquaculture varieties, aquaculture technology, environmental control technology, aquaculture equipment, and large-scale aquaculture platform construction technology. Specifically, the content of this Special Issue includes far-reaching marine aquaculture varieties and their aquaculture technologies, the influence of environmental stress on aquaculture varieties in complex aquaculture scenarios, the coupling motion characteristics of tank breeding platforms and tanks in wave environments, the construction technology for the safe and fish-suitable structures of far-reaching sea aquaculture platforms, new aquaculture technologies and methods, feed nutrition studies, aquaculture physiology research, etc. This Special Issue focuses on the development and progress of new aquaculture technologies and equipment through the latest research on far-reaching marine-suitable varieties and aquaculture facilities. These results will lead to further research and the extension of these techniques to other areas and species of aquaculture.

Dr. Zhenhua Ma
Prof. Dr. Jianguang Qin
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • offshore aquaculture biotechnology
  • water quality
  • fish behavior aquaculture
  • equipment aquaculture
  • working vessel
  • offshore floating aquaculture platform

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Published Papers (13 papers)

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Research

20 pages, 7676 KiB  
Article
Study on the Dynamic Response of Mooring System of Multiple Fish Cages under the Combined Effects of Waves and Currents
by Fuxiang Liu, Zhentao Jiang, Tianhu Cheng, Yuwang Xu, Haitao Zhu, Gang Wang, Guoqing Sun and Yuqin Zhang
J. Mar. Sci. Eng. 2024, 12(9), 1648; https://doi.org/10.3390/jmse12091648 - 14 Sep 2024
Viewed by 459
Abstract
Deep-sea aquaculture can alleviate the spatial and environmental pressure of near-shore aquaculture and produce higher quality aquatic products, which is the main development direction of global aquaculture. The coastline of China is relatively flat, with aquaculture operations typically operating in sea areas with [...] Read more.
Deep-sea aquaculture can alleviate the spatial and environmental pressure of near-shore aquaculture and produce higher quality aquatic products, which is the main development direction of global aquaculture. The coastline of China is relatively flat, with aquaculture operations typically operating in sea areas with water depths of approximately 30–50 m. However, with frequent typhoons and poor sea conditions, the design of mooring system has always been a difficult problem. This paper investigated the multiple cages, considering two layouts of 1 × 4 and 2 × 2, and proposed three different mooring system design schemes. The mooring line tension of the mooring systems under the self-storage condition was compared, and it was observed whether the mooring line accumulation and the contact between the mooring line and the steel structure occurred on the leeward side. Additionally, flexible net models were compared with rigid net models to evaluate the impact of net deformation on cage movement and mooring line tension. Finally, based on the optimal mooring design, the dynamic response of the mooring system under irregular wave conditions was analyzed and studied, providing important reference for the safety and economic design of the mooring system of multiple fish cages. Full article
(This article belongs to the Special Issue New Techniques and Equipment in Large Offshore Aquaculture Platform)
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Figure 1

Figure 1
<p>Numerical approach for hydrodynamic analysis of the fish cages.</p> Full article ">Figure 2
<p>Lumped mass method [<a href="#B29-jmse-12-01648" class="html-bibr">29</a>].</p> Full article ">Figure 3
<p>Single cage numerical calculation model.</p> Full article ">Figure 4
<p>Scheme 1: Layout of 1 × 4 cages and design of mooring system.</p> Full article ">Figure 5
<p>Scheme 2: Layout of 1 × 4 cages and design of inclined mooring system.</p> Full article ">Figure 5 Cont.
<p>Scheme 2: Layout of 1 × 4 cages and design of inclined mooring system.</p> Full article ">Figure 6
<p>Scheme 3: Layout of 2 × 2 cages and design of mooring system.</p> Full article ">Figure 7
<p>Scheme 1 Maximum mooring line tension under 0° wave-current incidence.</p> Full article ">Figure 8
<p>Deformation of nets in Scheme 1.</p> Full article ">Figure 9
<p>Scheme 1. Maximum mooring line tension under 45° wave-current incidence.</p> Full article ">Figure 10
<p>Scheme 1. Maximum mooring line tension under 90° wave-current incidence.</p> Full article ">Figure 11
<p>Scheme 1. Side view of cable status when large displacement occurs (0° wave-current incidence).</p> Full article ">Figure 12
<p>Scheme 2. Maximum mooring line tension under 0° wave-current incidence.</p> Full article ">Figure 13
<p>Scheme 2. Maximum mooring line tension under 45° wave-current incidence.</p> Full article ">Figure 14
<p>Scheme 2. Maximum mooring line tension under 90° wave-current incidence.</p> Full article ">Figure 15
<p>Scheme 2. Side view of cable status when large displacement occurs (90° wave-current incidence).</p> Full article ">Figure 16
<p>Scheme 3. Maximum mooring line tension under 0° wave-current incidence.</p> Full article ">Figure 17
<p>Scheme 3. Maximum mooring line tension under 45° wave-current incidence.</p> Full article ">Figure 18
<p>Response to the surge motion.</p> Full article ">Figure 19
<p>Heave motion response.</p> Full article ">Figure 19 Cont.
<p>Heave motion response.</p> Full article ">Figure 20
<p>Maximum cable tension.</p> Full article ">Figure 21
<p>Motion of Fish Cage1.</p> Full article ">Figure 22
<p>Comparison of motion spectrum.</p> Full article ">Figure 23
<p>Tension on mooring chain and connect cable.</p> Full article ">Figure 24
<p>Tension spectrum of mooring chain and connecting cable.</p> Full article ">Figure 25
<p>Fitting of Gumbel distribution for mooring line at 0° wave-current incidence direction.</p> Full article ">Figure 26
<p>Fitting of Gumbel distribution for mooring line at 45° wave-current incidence direction.</p> Full article ">
25 pages, 28511 KiB  
Article
A Method for Estimating the Distribution of Trachinotus ovatus in Marine Cages Based on Omnidirectional Scanning Sonar
by Yu Hu, Jiazhen Hu, Pengqi Sun, Guohao Zhu, Jialong Sun, Qiyou Tao, Taiping Yuan, Gen Li, Guoliang Pang and Xiaohua Huang
J. Mar. Sci. Eng. 2024, 12(9), 1571; https://doi.org/10.3390/jmse12091571 - 6 Sep 2024
Viewed by 377
Abstract
In order to accurately estimate the distribution of Trachinotus ovatus in marine cages, a novel method was developed using omnidirectional scanning sonar and deep-learning techniques. This method involved differentiating water layers and clustering data layer by layer to achieve precise location estimation. The [...] Read more.
In order to accurately estimate the distribution of Trachinotus ovatus in marine cages, a novel method was developed using omnidirectional scanning sonar and deep-learning techniques. This method involved differentiating water layers and clustering data layer by layer to achieve precise location estimation. The approach comprised two main components: fish identification and fish clustering. Firstly, omnidirectional scanning sonar was employed to perform spiral detection within marine cages, capturing fish image data. These images were then labeled to construct a training dataset for an enhanced CS-YOLOv8s model. After training, the CS-YOLOv8s model was used to identify and locate fish within the images. Secondly, the cages were divided into water layers with depth intervals of 40 cm. The identification coordinate data for each water layer were clustered using the DBSCAN method to generate location coordinates for the fish in each layer. Finally, the coordinate data from all water layers were consolidated to determine the overall distribution of fish within the cage. This method was shown, through multiple experimental results, to effectively estimate the distribution of Trachinotus ovatus in marine cages, closely matching the distributions detected manually. Full article
(This article belongs to the Special Issue New Techniques and Equipment in Large Offshore Aquaculture Platform)
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<p>Satellite image map of the experimental site.</p> Full article ">Figure 2
<p>Aerial view of the experimental site.</p> Full article ">Figure 3
<p>Cage used in the experiment.</p> Full article ">Figure 4
<p><span class="html-italic">Trachinotus ovatus</span> used in the experiment. (<b>a</b>) Body length; (<b>b</b>) Body height.</p> Full article ">Figure 5
<p>Working principle diagram of omnidirectional scanning sonar. (<b>a</b>) Schematic diagram of sonar scanning; (<b>b</b>) Work diagram.</p> Full article ">Figure 6
<p>Omnidirectional scanning sonar.</p> Full article ">Figure 7
<p>Sonar Assembly.</p> Full article ">Figure 8
<p>Side view of the sea cage. The sonar in the yellow box is located on the center axis of the net cage, and the yellow dotted lines shows the range covered by the sonar.</p> Full article ">Figure 9
<p>Training process (Loss).</p> Full article ">Figure 10
<p>Training process (mAP@0.5%).</p> Full article ">Figure 11
<p>Coordinate transformation. (<b>a</b>) A coordinate system with sonar as the origin; (<b>b</b>) A coordinate system with the top left corner as the origin.</p> Full article ">Figure 12
<p>Water layer division.</p> Full article ">Figure 13
<p>Clustering in the 100–140 cm water layer. (<b>a</b>) Fish distribution; (<b>b</b>) Cluster effect diagram.</p> Full article ">Figure 14
<p>Water layer data and the data after clustering. (<b>a</b>) Fish distribution map before clustering; (<b>b</b>) Cluster diagram of noisy points.</p> Full article ">Figure 15
<p>Center and noise point of core object. (<b>a</b>) Distribution map of center point and noise point; (<b>b</b>) Centralization rendering.</p> Full article ">Figure 16
<p>Sonar images of different numbers of fish. (<b>a</b>) 100 fish; (<b>b</b>) 150 fish; (<b>c</b>) 200 fish.</p> Full article ">Figure 17
<p>Fish for object identification. (<b>a</b>) 100 fish; (<b>b</b>) 150 fish; (<b>c</b>) 200 fish.</p> Full article ">Figure 18
<p>Trend of number of fish with depth. (<b>a</b>) Comparison bar chart of depth distribution; (<b>b</b>) Comparison line chart of fish depth distribution.</p> Full article ">Figure 19
<p>Horizontal distribution maps of fish. (<b>a</b>) Horizontal distribution of 100 fish; (<b>b</b>) Horizontal distribution of 150 fish; (<b>c</b>) Horizontal distribution of 200 fish.</p> Full article ">Figure 20
<p>Horizontal distribution comparison chart of fish.</p> Full article ">Figure 21
<p>Spatial distribution maps of 100 Fish in each water layer. (<b>a</b>) Water layer: 0–40 cm; (<b>b</b>) Water layer: 25–65 cm; (<b>c</b>) Water layer: 50–90 cm; (<b>d</b>) Water layer: 75–115 cm; (<b>e</b>) Water layer: 100–140 cm; (<b>f</b>) Water layer: 125–165 cm; (<b>g</b>) Water layer: 150–190 cm; (<b>h</b>) Water layer: 175–215 cm; (<b>i</b>) Water layer: 200–240 cm; (<b>j</b>) Water layer: 225–265 cm; (<b>k</b>) Water layer: 250–290 cm.</p> Full article ">Figure 22
<p>Spatial distribution maps of 150 Fish in each water layer. (<b>a</b>) Water layer: 0–40 cm; (<b>b</b>) Water layer: 25–65 cm; (<b>c</b>) Water layer: 50–90 cm; (<b>d</b>) Water layer: 75–115 cm; (<b>e</b>) Water layer: 100–140 cm; (<b>f</b>) Water layer: 125–165 cm; (<b>g</b>) Water layer: 150–190 cm; (<b>h</b>) Water layer: 175–215 cm; (<b>i</b>) Water layer: 200–240 cm; (<b>j</b>) Water layer: 225–265 cm; (<b>k</b>) Water layer: 250–290 cm.</p> Full article ">Figure 23
<p>Spatial distribution maps of 200 fish in each water layer. (<b>a</b>) Water layer: 0–40 cm; (<b>b</b>) Water layer: 25–65 cm; (<b>c</b>) Water layer: 50–90 cm; (<b>d</b>) Water layer: 75–115 cm; (<b>e</b>) Water layer: 100–140 cm; (<b>f</b>) Water layer: 125–165 cm; (<b>g</b>) Water layer: 150–190 cm; (<b>h</b>) Water layer: 175–215 cm; (<b>i</b>) Water layer: 200–240 cm; (<b>j</b>) Water layer: 225–265 cm; (<b>k</b>) Water layer: 250–290 cm.</p> Full article ">Figure 24
<p>3D spatial distribution maps of cage fish. (<b>a</b>) Spatial distribution of 100 fish group; (<b>b</b>) Spatial distribution of 150 fish group; (<b>c</b>) Spatial distribution of 200 fish group.</p> Full article ">Figure 25
<p>Spatial distribution comparison chart of fish.</p> Full article ">Figure 26
<p>Distribution of fish at different temperatures. (<b>a</b>) Distribution of 100 fish; (<b>b</b>) Distribution of 150 fish; (<b>c</b>) Distribution of 200 fish.</p> Full article ">
15 pages, 5194 KiB  
Article
The Effect of Corner Structure on the Optimisation of Fishable Flow Field in Aquaculture Tanks
by Fan Zhang, Mingchao Cui, Huang Liu and Chen Zhang
J. Mar. Sci. Eng. 2024, 12(7), 1185; https://doi.org/10.3390/jmse12071185 - 15 Jul 2024
Viewed by 622
Abstract
As coastal waters face constraints such as the deterioration of the aquaculture environment and limitations on the scale of operation, aquaculture will move towards the deep and distant sea. Large-scale aquaculture vessels are a new method of deep-sea aquaculture, and improving the utilisation [...] Read more.
As coastal waters face constraints such as the deterioration of the aquaculture environment and limitations on the scale of operation, aquaculture will move towards the deep and distant sea. Large-scale aquaculture vessels are a new method of deep-sea aquaculture, and improving the utilisation efficiency of aquaculture tanks to ensure the best growth conditions for fish inside while ensuring the efficient discharge of particulate matter in these tanks will affect the productivity of aquaculture and the profitability of aquaculture vessels. This study investigated the effects of the tank structure ratio on the flow field characteristics and particulate removal efficiency in the aquaculture tanks of an aquaculture vessel. Numerical simulations of the flow field characteristics in the aquaculture tanks of an 8000 t-class aquaculture vessel at anchor were conducted using the FLOW-3D software to quantitatively evaluate the effects of the corner ratio on the fishability of aquaculture tanks and the efficiency of particulate emission using the parameters related to flow velocity, turbulence intensity, capacity utilisation rate, and particulate removal efficiency. The simulation results show that the tanks with corner structures have better flow field characteristics, which include a higher flow velocity, turbulence intensity, and discharge effect. When the corner length is more than 1/3 of the tank length, increasing the corner distance does not significantly enhance the optimisation of the flow field characteristics in the tank. Overall, this study’s results provide a reference basis for the structural design and optimisation of aquaculture tanks in aquaculture vessels. Full article
(This article belongs to the Special Issue New Techniques and Equipment in Large Offshore Aquaculture Platform)
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<p>Sketch and marks of the aquaculture fish vessel.</p> Full article ">Figure 2
<p>(<b>a</b>) Rectangular tank geometry model for numerical model validation; (<b>b</b>–<b>d</b>) show the variation curves of the flow velocity in Probes 1, 2, and 3, respectively.</p> Full article ">Figure 3
<p>Situation groups.</p> Full article ">Figure 4
<p>Probability density distribution of the streamflow and proportion of fishable area at each corner ratio.</p> Full article ">Figure 5
<p>(<b>a</b>) The variation in the statistical flow rate versus the variation in the residence time. (<b>b</b>) The variation in the drag coefficient <span class="html-italic">C<sub>t</sub></span> versus the capacity utilisation efficiency in the tank.</p> Full article ">Figure 6
<p>Flow velocity cloud at Z = 7.5 m, Z = 4.5 m, and Z = 0.5 m profiles for different corner scales.</p> Full article ">Figure 7
<p>Vector diagram of the flow field at the corners.</p> Full article ">Figure 8
<p>Efficiency of particulate exclusion in tanks.</p> Full article ">Figure 9
<p>Characteristics of vortex strength for different corner ratios.</p> Full article ">
21 pages, 10058 KiB  
Article
An Experimental Study on Estimating the Quantity of Fish in Cages Based on Image Sonar
by Guohao Zhu, Mingyang Li, Jiazhen Hu, Luyu Xu, Jialong Sun, Dazhang Li, Chao Dong, Xiaohua Huang and Yu Hu
J. Mar. Sci. Eng. 2024, 12(7), 1047; https://doi.org/10.3390/jmse12071047 - 21 Jun 2024
Viewed by 723
Abstract
To address the highly demanding assessment of the quantity of fish in cages, a method for estimating the fish quantity in cages based on image sonar is proposed. In this method, forward-looking image sonar is employed for continuous detection in cages, and the [...] Read more.
To address the highly demanding assessment of the quantity of fish in cages, a method for estimating the fish quantity in cages based on image sonar is proposed. In this method, forward-looking image sonar is employed for continuous detection in cages, and the YOLO target detection model with attention mechanism as well as a BP neural network are combined to achieve a real-time automatic estimation of fish quantity in cages. A quantitative experiment was conducted in the South China Sea to render a database for training the YOLO model and neural network. The experimental results show that the average detection accuracy mAP50 of the improved YOLOv8 is 3.81% higher than that of the original algorithm. The accuracy of the neural network in fitting the fish quantity reaches 84.63%, which is 0.72% better than cubic polynomial fitting. In conclusion, the accurate assessment of the fish quantity in cages contributes to the scientific and intelligent management of aquaculture and the rational formulation of feeding and fishing plans. Full article
(This article belongs to the Special Issue New Techniques and Equipment in Large Offshore Aquaculture Platform)
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Figure 1

Figure 1
<p>ARIS1800 sonar physical diagram.</p> Full article ">Figure 2
<p>Schematic diagram of sonar fish detection.</p> Full article ">Figure 3
<p>Satellite image of the experimental sea area.</p> Full article ">Figure 4
<p>Aerial image of the experimental base.</p> Full article ">Figure 5
<p>Experimental cage.</p> Full article ">Figure 6
<p>Schematic diagram of the sonar deployment.</p> Full article ">Figure 7
<p>Sonar images of different groups of fish. (<b>a</b>) Twenty fish; (<b>b</b>) forty fish; (<b>c</b>) sixty fish; and (<b>d</b>) eighty fish.</p> Full article ">Figure 8
<p>YOLO feature extraction network.</p> Full article ">Figure 9
<p>CSAM module.</p> Full article ">Figure 10
<p>Training process (loss).</p> Full article ">Figure 11
<p>Training process (mAP50).</p> Full article ">Figure 12
<p>Comparison of detection images. (<b>a</b>–<b>c</b>) our algorithm; (<b>d</b>–<b>f</b>) YOLOv8.</p> Full article ">Figure 13
<p>Topology of the neural network.</p> Full article ">Figure 14
<p>Statistical diagram of the maximum quantity detected.</p> Full article ">Figure 15
<p>The sample mean square error.</p> Full article ">Figure 16
<p>Prediction error of the training group and the test group.</p> Full article ">Figure 17
<p>Regression results of the BP neural network model.</p> Full article ">Figure 18
<p>Comparison of the data fitting results. (<b>a</b>) Linear fitting; (<b>b</b>) cubic polynomial fitting.</p> Full article ">Figure 19
<p>Comparison of the fitting results of higher order polynomials. (<b>a</b>) Quadratic polynomial; (<b>b</b>) cubic polynomial; and (<b>c</b>) quartic polynomial.</p> Full article ">Figure 20
<p>Estimation and error bar chart of the fitting test.</p> Full article ">
11 pages, 1176 KiB  
Article
Effect of Licorice on Gene Expression Related to the Growth of Asian Seabass Lates calcarifer
by Rui Yang, Wang Zhao, Yifu Wang, Zhengyi Fu, Jing Hu, Shengjie Zhou, Minghao Li and Zhenhua Ma
J. Mar. Sci. Eng. 2024, 12(7), 1036; https://doi.org/10.3390/jmse12071036 - 21 Jun 2024
Viewed by 623
Abstract
The Asian seabass (Lates calcarifer) has high economic value and is the primary aquaculture species in China. Licorice (Glycyrrhiza uralensis) as a feed additive has demonstrated significant immunological benefits in aquaculture. However, its effects on the growth of aquatic animals [...] Read more.
The Asian seabass (Lates calcarifer) has high economic value and is the primary aquaculture species in China. Licorice (Glycyrrhiza uralensis) as a feed additive has demonstrated significant immunological benefits in aquaculture. However, its effects on the growth of aquatic animals are largely unexplored. This study explored the influence of licorice on the level of growth-related genes in Asian seabass by conducting an experiment using artificial feed with 0%, 1%, 3%, and 5% licorice. The impact on growth performance and the expression of several genes, including growth hormone–releasing hormone (GHRH), growth hormone (GH), growth hormone receptor (GHR), insulin-like growth factor 1 (IGF1), IGF2, IGF2 receptor (IGF2R), myostatin 1 (MSTN1), and myostatin 2 (MSTN2), were studied over 56 d. According to the results, the 3% and 5% licorice-supplemented diets significantly improved survival rates and weight gain compared to the control group. Licorice affected the level of growth-associated genes in Asian seabass and significantly increased the levels of GHR and IGF1 in the liver. However, a 5% licorice diet downregulated the expression of IGF2. As the licorice content in the diet increased, the levels of IGF2R and MSTN1 in the muscle tissue first decreased and then increased, and licorice addition inhibited the MSTN2 expression. The inclusion of licorice in the feed led to a significant downregulation of the GH and GHRH expression (p < 0.05). In summary, adding a certain proportion of licorice to the diet can improve the survival rate of the Asian seabass. Moreover, a proper proportion of licorice can increase the expression of related growth genes of fish, effectively increasing their weight gain rate and specific growth rate. Full article
(This article belongs to the Special Issue New Techniques and Equipment in Large Offshore Aquaculture Platform)
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<p>Effects of licorice on survival rate and growth of Asian seabass. (<b>A</b>) survival, (<b>B</b>) weight gain (WG), (<b>C</b>) feed intake (FI), (<b>D</b>) specific growth rate (SGR); means within rows with the same superscript are not significantly different (<span class="html-italic">p</span> &gt; 0.05), while the different letters mean significant differences (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 2
<p>The relative effect of licorice on the level of growth-related genes in the liver tissue. Note: Different letters indicate significant differences. Subscripts 1, 2, and 3 represent <span class="html-italic">GHR</span>, <span class="html-italic">IGF1</span>, and <span class="html-italic">IGF2</span>, respectively.</p> Full article ">Figure 3
<p>The relative effect of <span class="html-italic">Glycyrrhiza uralensis</span> on the level of growth-associated genes in the muscle tissue. Note: Different letters indicate significant differences. Subscripts 1, 2, and 3 represent <span class="html-italic">IGF2R</span>, <span class="html-italic">MSTN1</span>, and <span class="html-italic">MSTN2</span>, respectively.</p> Full article ">Figure 4
<p>The relative effect of <span class="html-italic">Glycyrrhiza uralensis</span> on the level of growth-associated genes in the brain tissue. Note: The letters indicate significant differences. Subscripts 1 and 2 represent <span class="html-italic">GH</span>, and <span class="html-italic">GHRH</span>, respectively.</p> Full article ">
17 pages, 8157 KiB  
Article
The Effects of Acute Ammonia Nitrogen Stress on Antioxidant Ability, Phosphatases, and Related Gene Expression in the Kidney of Juvenile Yellowfin Tuna (Thunnus albacares)
by Yongyue Sun, Zhengyi Fu and Zhenhua Ma
J. Mar. Sci. Eng. 2024, 12(6), 1009; https://doi.org/10.3390/jmse12061009 - 18 Jun 2024
Viewed by 717
Abstract
This study investigated the effects of acute ammonia nitrogen (NH3-N) exposure on kidney antioxidant ability and phosphatases and related gene expression in juvenile yellowfin tuna (Thunnus albacares). The 180 juvenile yellowfin tuna (260.39 ± 55.99 g, 22.33 ± 2.28 [...] Read more.
This study investigated the effects of acute ammonia nitrogen (NH3-N) exposure on kidney antioxidant ability and phosphatases and related gene expression in juvenile yellowfin tuna (Thunnus albacares). The 180 juvenile yellowfin tuna (260.39 ± 55.99 g, 22.33 ± 2.28 cm) were exposed to ammonia for 6, 24, and 36 h using natural seawater (0 mg/L) as a control and NH3-N at 5 and 10 mg/L. The lipid peroxidation byproduct malondialdehyde (MDA) and the levels of antioxidant enzymes, such as superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GSH-PX), alkaline phosphatase (AKP), and acid phosphatase (ACP), were measured using the colorimetric method in the trunk kidney to determine changes in antioxidant ability and phosphatase activity of juvenile yellowfin tuna exposed to NH3-N. Results indicated that, at 36 h, MDA, SOD, CAT, and GSH-PX levels rose in the 5 mg/L group versus the control. In the 10 mg/L group, MDA and SOD, CAT, and GSH-PX activities significantly increased after 24 and 36 h exposure compared to the control. Phosphatases play a pivotal role in the immune system. AKP activity significantly increased at 6 h, and ACP activity markedly rose at 36 h in the 5 mg/L group versus the control. Real-time fluorescence quantitative PCR was applied to detect alterations in the antioxidant genes SOD2, CAT, and glutathione peroxidase 1b (GPX1b) and immune cytokines-related genes Interleukin 10 (IL-10) and Interleukin 6 receptor (IL-6r) expression in the head kidney in juvenile tuna. Relative to the control, antioxidant gene expression in the 5 mg/L group significantly rose at 6 and 36 h, and in the 10 mg/L group, SOD2 and GPX1b were significantly elevated at 36 h. Compared to the control group, IL-10 expression in the 5 mg/L group significantly increased at 6 h, whereas IL-6r expression decreased. In the 10 mg/L group, both IL-10 and IL-6r levels were observed to be lower. Low ammonia nitrogen concentrations boost antioxidant defenses, phosphatase activities, and gene expression levels, whereas higher levels may induce suppressive effects. In yellowfin tuna juvenile farming, NH3-N concentration significantly affects the health of the juveniles. When the NH3-N concentration is between 5–10 mg/L, the stress duration should be limited to 24 h; if the concentration is below 5 mg/L, the stress duration can be extended to 36 h. Full article
(This article belongs to the Special Issue New Techniques and Equipment in Large Offshore Aquaculture Platform)
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<p>Effect of acute ammonia nitrogen stress on the antioxidant ability in the trunk kidney of juvenile yellowfin tuna (<span class="html-italic">n</span> = 9). (<b>a</b>) Difference value in malondialdehyde (MDA) concentration (treatment-control), (<b>b</b>) MDA concentration in treatment and control groups, (<b>c</b>) difference value in superoxide dismutase (SOD) activity (treatment-control), (<b>d</b>) SOD activity in treatment and control groups, (<b>e</b>) difference value in catalase (CAT) activity (treatment-control), (<b>f</b>) CAT activity in treatment and control groups, (<b>g</b>) difference value in glutathione peroxidase (GSH-PX) activity (treatment-control), (<b>h</b>) GSH-PX activity in treatment and control groups. The difference value = treatment value − control mean value. Different letters signify the significance of varying ammonia concentrations at the same time point (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 1 Cont.
<p>Effect of acute ammonia nitrogen stress on the antioxidant ability in the trunk kidney of juvenile yellowfin tuna (<span class="html-italic">n</span> = 9). (<b>a</b>) Difference value in malondialdehyde (MDA) concentration (treatment-control), (<b>b</b>) MDA concentration in treatment and control groups, (<b>c</b>) difference value in superoxide dismutase (SOD) activity (treatment-control), (<b>d</b>) SOD activity in treatment and control groups, (<b>e</b>) difference value in catalase (CAT) activity (treatment-control), (<b>f</b>) CAT activity in treatment and control groups, (<b>g</b>) difference value in glutathione peroxidase (GSH-PX) activity (treatment-control), (<b>h</b>) GSH-PX activity in treatment and control groups. The difference value = treatment value − control mean value. Different letters signify the significance of varying ammonia concentrations at the same time point (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 2
<p>Effect of acute ammonia nitrogen stress on the activity of phosphatase in the trunk kidney of juvenile yellowfin tuna (<span class="html-italic">n</span> = 9). (<b>a</b>) Difference value in alkaline phosphatase (AKP) activity (treatment-control), (<b>b</b>) AKP activity in treatment and control groups, (<b>c</b>) difference value in acid phosphatase (ACP) activity (treatment-control), (<b>d</b>) ACP activity in treatment and control groups. The difference value = treatment value − control mean value. Different letters signify the significance of varying ammonia concentrations at the same time point (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 3
<p>Effects of acute ammonia nitrogen stress on antioxidant genes in the head kidney of juvenile yellowfin tuna (<span class="html-italic">n</span> = 9). (<b>a</b>) Difference value in superoxide dismutase 2 (SOD2) mRNA expression level (treatment-control), (<b>b</b>) SOD2 mRNA expression level in treatment and control groups, (<b>c</b>) difference value in catalase (CAT) mRNA expression level (treatment-control), (<b>d</b>) CAT mRNA expression level in treatment and control groups, (<b>e</b>) difference value in glutathione peroxidase 1b (GPX1b) mRNA expression level (treatment-control), (<b>f</b>) GPX1b mRNA expression level in treatment and control groups. The difference value = treatment value − control mean value. Different letters signify the significance of varying ammonia concentrations at the same time point (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 3 Cont.
<p>Effects of acute ammonia nitrogen stress on antioxidant genes in the head kidney of juvenile yellowfin tuna (<span class="html-italic">n</span> = 9). (<b>a</b>) Difference value in superoxide dismutase 2 (SOD2) mRNA expression level (treatment-control), (<b>b</b>) SOD2 mRNA expression level in treatment and control groups, (<b>c</b>) difference value in catalase (CAT) mRNA expression level (treatment-control), (<b>d</b>) CAT mRNA expression level in treatment and control groups, (<b>e</b>) difference value in glutathione peroxidase 1b (GPX1b) mRNA expression level (treatment-control), (<b>f</b>) GPX1b mRNA expression level in treatment and control groups. The difference value = treatment value − control mean value. Different letters signify the significance of varying ammonia concentrations at the same time point (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 4
<p>Effects of acute ammonia nitrogen stress on immune-related genes in the head kidney of juvenile yellowfin tuna (<span class="html-italic">n</span> = 9). (<b>a</b>) Difference value in Interleukin 10 (IL-10) mRNA expression level (treatment-control), (<b>b</b>) IL-10 mRNA expression level in treatment and control groups, (<b>c</b>) difference value in Interleukin 6 receptor (IL-6r) mRNA expression level (treatment-control), (<b>d</b>) IL-6r gene expression levels in treatment and control groups. The difference value = treatment value − control mean value. Different letters signify the significance of varying ammonia concentrations at the same time point (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">
13 pages, 1638 KiB  
Article
Antioxidant and Metabolic Response to Acute Acidification Stress of Juvenile Yellowfin Tuna (Thunnus albacares)
by Xiaoyan Wang, Rui Yang, Zhengyi Fu, Lei Zhao and Zhenhua Ma
J. Mar. Sci. Eng. 2024, 12(6), 970; https://doi.org/10.3390/jmse12060970 - 8 Jun 2024
Viewed by 898
Abstract
This study aimed to explore the impact of acute acidification on the antioxidant, metabolic performance, and liver histology of juvenile yellowfin tuna. The experiment subjected juvenile yellowfin tuna to a pH gradient environment of 8.1, 7.6, 7.1, and 6.6 for 48 h. The [...] Read more.
This study aimed to explore the impact of acute acidification on the antioxidant, metabolic performance, and liver histology of juvenile yellowfin tuna. The experiment subjected juvenile yellowfin tuna to a pH gradient environment of 8.1, 7.6, 7.1, and 6.6 for 48 h. The findings indicate that a seawater pH of 7.1 significantly impacts the antioxidant and metabolic systems of the juvenile yellowfin tuna in comparison to the control group. At pH 7.1, there were observed increases in glutathione reductase (GR), total antioxidant capacity (T-AOC), lactate dehydrogenase (LDH), hexokinase (HK), pyruvate kinase (PK), sodium-potassium ATPase (Na+K+-ATP), and calcium-magnesium ATPase (Ca2+Mg2+-ATP). Conversely, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TGs) were not significantly different across the treatment groups. However, an increase in transaminases at pH 7.1 suggested potential liver damage, which was further supported by observed structural liver tissue degeneration and hepatocyte vacuolation. In conclusion, under conditions of acute acidification stress, there is a decrease in antioxidant capacity and a suppression of metabolic levels in juvenile yellowfin tuna, leading to oxidative damage. This study lays the foundation for an in-depth understanding of the response mechanisms of juvenile yellowfin tuna in response to seawater acidification as well as healthy tuna farming in the broader context of seawater acidification. Full article
(This article belongs to the Special Issue New Techniques and Equipment in Large Offshore Aquaculture Platform)
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<p>Effect of acute acidification stress on antioxidant parameters in juvenile yellowfin tuna liver. The bar graph represents the mean ± SD of the measurements taken at 48 h. (<b>a</b>) Glutathione reductase (GR) activity (ANOVA, GR: F = 10.968, <span class="html-italic">p</span> &lt; 0.05), (<b>b</b>) total antioxidant (T-AOC) capacity (ANOVA, T-AOC: F = 2.396, <span class="html-italic">p</span> &gt; 0.05), and (<b>c</b>) lipid peroxidation (LPO) (ANOVA, LPO: F = 10.714, <span class="html-italic">p</span> &lt; 0.05). Different letters on the columns indicate significant differences between groups (<span class="html-italic">p</span> &lt; 0.05), and the same letters indicate non-significant differences between groups (<span class="html-italic">p</span> &gt; 0.05).</p> Full article ">Figure 2
<p>Effect of acute acidification stress on metabolic enzymes in juvenile yellowfin tuna liver. The bar graph represents the mean ± SD of the measurements taken at 48 h. (<b>a</b>) Lactate dehydrogenase (LDH) activity (ANOVA, LDH: F = 24.458, <span class="html-italic">p</span> &lt; 0.05), (<b>b</b>) hexokinase (HK) activity (ANOVA, HK: F = 12.447, <span class="html-italic">p</span> &lt; 0.05), (<b>c</b>) pyruvate kinase (PK) activity (ANOVA, PK: F = 28.622, <span class="html-italic">p</span> &lt; 0.05), (<b>d</b>) sodium-potassium ATPase (Na<sup>+</sup>K<sup>+</sup>-ATP) activity (ANOVA, Na<sup>+</sup>K<sup>+</sup>-ATP: F = 25.508, <span class="html-italic">p</span> &lt; 0.05), and (<b>e</b>) calcium-magnesium ATPase (Ca<sup>2+</sup>Mg<sup>2+</sup>-ATP) activity (ANOVA, Ca<sup>2</sup><sup>+</sup>Mg<sup>2</sup><sup>+</sup>-ATP: F = 347.865, <span class="html-italic">p</span> &lt; 0.05). Different letters on the columns indicate significant differences between groups (<span class="html-italic">p</span> &lt; 0.05), and the same letters indicate non-significant differences between groups (<span class="html-italic">p</span> &gt; 0.05).</p> Full article ">Figure 3
<p>Effect of acidification stress on serum indices of juvenile yellowfin tuna. The bar graph represents the mean ± SD of the measurements taken at 48 h. (<b>a</b>) Glucose (GLU) content (ANOVA, GLU: F = 148.637, <span class="html-italic">p</span> &lt; 0.05), (<b>b</b>) low-density lipoprotein cholesterol (LDL-C) content (ANOVA, LDH-C: F = 3.385, <span class="html-italic">p</span> &gt; 0.05), (<b>c</b>) high-density lipoprotein cholesterol (HDL-C) content (ANOVA, HDH-C: F = 1.148, <span class="html-italic">p</span> &gt; 0.05), (<b>d</b>) triglycerides (TGs) content (ANOVA, TG: F = 2.688, <span class="html-italic">p</span> &gt; 0.05), (<b>e</b>) total cholesterol (TCH) content (ANOVA, TCH: F = 1.209, <span class="html-italic">p</span> &gt; 0.05), (<b>f</b>) glutamic oxaloacetic transaminase (GOT) activity (ANOVA, GOT: F = 57.086, <span class="html-italic">p</span> &lt; 0.05), (<b>g</b>) glutamine pyruvate transaminase (GPT) activity (ANOVA, GPT: F = 4.197, <span class="html-italic">p</span> &lt; 0.05), and (<b>h</b>) alkaline phosphatase (AKP) activity (ANOVA, AKP: F = 18.039, <span class="html-italic">p</span> &lt; 0.05). Different letters on the columns indicate significant differences between groups (<span class="html-italic">p</span> &lt; 0.05), and the same letters indicate non-significant differences between groups (<span class="html-italic">p</span> &gt; 0.05).</p> Full article ">Figure 4
<p>Effect of acute acidification stress of yellowfin tuna on liver histology (400×). (<b>a</b>) The pH 8.1 treatment group (control), (<b>b</b>) the pH 7.6 treatment group, (<b>c</b>) the pH 7.1 treatment group, and (<b>d</b>) the pH 6.6 treatment group. Red arrows indicate hepatocyte nuclei; red circles indicate blurred hepatocyte structures; yellow arrows indicate sinusoidal gaps; yellow circles indicate loss of nuclei; green arrows indicate rounded vacuoles; green circles indicate abnormal cellular morphology; and blue circles indicate nuclear excursions.</p> Full article ">
16 pages, 4024 KiB  
Article
Towards Fish Welfare in the Presence of Robots: Zebrafish Case
by Andrea Pino, Rosario Vidal, Elisabeth Tormos, José Miguel Cerdà-Reverter, Raúl Marín Prades and Pedro J. Sanz
J. Mar. Sci. Eng. 2024, 12(6), 932; https://doi.org/10.3390/jmse12060932 - 31 May 2024
Viewed by 716
Abstract
Zebrafish (Danio rerio) have emerged as a valuable animal model for neurobehavioral research, particularly in the study of anxiety-related states. This article explores the use of conceptual models to investigate stress, fear, and anxiety in zebrafish induced by bio-inspired mini-robotic fish with different [...] Read more.
Zebrafish (Danio rerio) have emerged as a valuable animal model for neurobehavioral research, particularly in the study of anxiety-related states. This article explores the use of conceptual models to investigate stress, fear, and anxiety in zebrafish induced by bio-inspired mini-robotic fish with different components and designs. The objective is to optimize robotic biomimicry and its impact on fish welfare. Previous studies have focused on externally controlled fish models, whereas this study introduces prototypes of freely actuated swimming robots to examine interactions between a bio-inspired robot and individual zebrafish. By means of analysis of behavioral responses, certain robotic components have been identified as potential causes of anxiety in fish, which have provided insights that may be applicable to other species and future aquacultural robot designs. Full article
(This article belongs to the Special Issue New Techniques and Equipment in Large Offshore Aquaculture Platform)
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<p>Real images of the four prototypes used and their comparison in size and appearance with a real zebrafish of the same size and aspect as those used in the tests.</p> Full article ">Figure 2
<p>Manufacturing and construction of robotic prototypes: (<b>a</b>) stereolithography printing of watertight housings for the prototypes; (<b>b</b>) parts and electronic components of prototypes A and B.</p> Full article ">Figure 3
<p>Electronic schematics of (<b>a</b>) Prototypes A and B; (<b>b</b>) Prototype C.</p> Full article ">Figure 4
<p>Recording setup of the top (<b>a</b>) and frontal (<b>b</b>) planes and tracking of the individuals using the AnimalTA software. Overhead (<b>c</b>) and frontal (<b>d</b>) views of the tank with the aversive zones marked in green: perimeter area from the top view equivalent to the space between the tank edges and its parallel projection at a distance of 2.5 cm; and top zone considered as the top half of the frontal view of the tank.</p> Full article ">Figure 5
<p>Reference velocities of how fish behaved when interacting with the different prototypes—(<b>a</b>) A; (<b>b</b>) B; (<b>c</b>) C; (<b>d</b>) D—and (<b>e</b>) fish alone.</p> Full article ">Figure 6
<p>Reference velocities of how fish behaved when interacting with the prototypes: (<b>a</b>) Prototype A without freezing periods; (<b>b</b>) Prototype B without freezing periods; (<b>c</b>) Prototype C without freezing periods; (<b>d</b>) Prototype A with freezing periods; (<b>e</b>) Prototype B with freezing periods; (<b>f</b>) Prototype C with freezing periods.</p> Full article ">Figure 7
<p>Relationship between velocity and Euclidean distance between the fish and the robot over time for a representative case of each of the tested prototypes: (<b>a</b>) Prototype A; (<b>b</b>) Prototype B; (<b>c</b>) Prototype C; and (<b>d</b>) Prototype D.</p> Full article ">Figure 8
<p>Representative density plots of the position of the fish from the frontal plane when (<b>a</b>) exposed to Prototype A; (<b>b</b>) exposed to Prototype B; (<b>c</b>) exposed to Prototype C; (<b>d</b>) exposed to Prototype D; and (<b>e</b>) alone. The color scale represents the cumulative time spent in each zone of the tank.</p> Full article ">Figure 9
<p>Density plots of the position of the live fish from the superior plane when (<b>a</b>) exposed to Prototype A; (<b>b</b>) exposed to Prototype B; (<b>c</b>) exposed to Prototype C; (<b>d</b>) exposed to Prototype D; and (<b>e</b>) alone.</p> Full article ">Figure 10
<p>The diagrams display the distribution and central tendency of the numerical values (through quartiles) obtained from each of the prototypes for (<b>a</b>) time on the surface; (<b>b</b>) velocity deviation; (<b>c</b>) acceleration deviation. The asterisks indicate statistical significance between the indicated prototype and the reference group (Fish alone).</p> Full article ">
15 pages, 1234 KiB  
Article
The Photoperiod Significantly Influences the Growth Rate, Digestive Efficiency, Immune Response, and Antioxidant Activities in the Juvenile Scalloped Spiny Lobster (Panulirus homarus)
by Yinggang Wang, Rui Yang, Zhengyi Fu, Zhenhua Ma and Zemin Bai
J. Mar. Sci. Eng. 2024, 12(3), 389; https://doi.org/10.3390/jmse12030389 - 24 Feb 2024
Cited by 1 | Viewed by 1083
Abstract
This study aimed to elucidate the effects of different photoperiods (0 L:24 D, 6 L:18 D, 12 L:12 D, 18 L:6 D, 24 L:0 D, “Light (L) and Dark (D)”) on the growth performance and physiological responses of the juvenile scalloped spiny lobster [...] Read more.
This study aimed to elucidate the effects of different photoperiods (0 L:24 D, 6 L:18 D, 12 L:12 D, 18 L:6 D, 24 L:0 D, “Light (L) and Dark (D)”) on the growth performance and physiological responses of the juvenile scalloped spiny lobster (Panulirus homarus). Over a period of 56 days, parameters such as growth rate, digestive enzyme, immune enzyme, and antioxidant enzyme were meticulously evaluated in 90 lobsters subjected to these varying light conditions. The present study found no significant differences in survival rate (SR), molting frequency (MF), and meat yield production (MYP) among the various photoperiod treatments (p > 0.05). Notably, the highest weight gain rate (WGR) and specific growth rate (SGR) were observed under a 12 L:12 D photoperiod. In the continuous dark phase (0 L:24 D), pepsin (PEP) activity remained high in gastric tissues, while trypsin (TRYP) and chymotrypsin (CHT) activities reached the highest in hepatopancreas tissues. The α-amylase (AMS) activity in the hepatopancreas was most elevated under 18 L:6 D, and the optimal lipase (LPS) activity was recorded under 12 L:12 D. The activity of acid phosphatase (ACP) in the hepatopancreas was highest in the absence of light (0 L:24 D), whereas the activities of alkaline phosphatase (AKP) and lysozyme (LZM) were most effective under the 12 L:12 D photoperiod. The total antioxidant capacity (T-AOC), along with catalase (CAT) and superoxide dismutase (SOD) activities of the hepatopancreas reached the highest at 12 L:12 D. The highest activity of glutathione peroxidase (GSH-Px) was seen under 18 L:6 D. The concentration of malondialdehyde (MDA), a marker of oxidative stress, was found to be highest under 12 L:12 D. Consequently, this specific photoperiod is essential for achieving optimal growth and maintaining appropriate physiological balance in the scalloped spiny lobster during aquaculture. These findings provide a foundational guideline for establishing the lighting environment in the farming of the juvenile scalloped spiny lobster. Full article
(This article belongs to the Special Issue New Techniques and Equipment in Large Offshore Aquaculture Platform)
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<p>Effect of photoperiod on digestive enzyme activities in scalloped spiny lobsters (n = 18). Pepsin (<b>A</b>), trypsin (<b>B</b>), amylase (<b>C</b>), chymotrypsin (<b>D</b>), and lipase (<b>E</b>). Different superscripts letters indicate statistically significant differences between treatments (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 2
<p>Effect of photoperiod on the immune enzyme activity of scalloped spiny lobsters (n = 18). Acid phosphatase (<b>A</b>), alkaline phosphatase (<b>B</b>), lysozyme (<b>C</b>). Different superscripts letters indicate statistically significant differences between treatments (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">Figure 3
<p>Effect of photoperiod on antioxidant capacity of scalloped spiny lobsters (n = 18). Superoxide dismutase (<b>A</b>), peroxidase (<b>B</b>), glutathione peroxidase (<b>C</b>), total antioxidant capacity (<b>D</b>), malondialdehyde (<b>E</b>). Different superscripts letters indicate statistically significant differences between treatments (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">
22 pages, 9326 KiB  
Article
Sloshing Response of an Aquaculture Vessel: An Experimental Study
by Yanwu Tao, Renqing Zhu, Jiayang Gu, Qi Wei, Fangxin Hu, Xiaosen Xu, Zhongyu Zhang and Zhiyu Li
J. Mar. Sci. Eng. 2023, 11(11), 2122; https://doi.org/10.3390/jmse11112122 - 6 Nov 2023
Cited by 2 | Viewed by 1142
Abstract
The sloshing response is crucial to the design and operation of aquaculture vessels and affects the safety of the culture equipment and the efficiency of the culture operation. A 1/50 scaled model was utilized to investigate the coupled sloshing response characteristics of a [...] Read more.
The sloshing response is crucial to the design and operation of aquaculture vessels and affects the safety of the culture equipment and the efficiency of the culture operation. A 1/50 scaled model was utilized to investigate the coupled sloshing response characteristics of a novel aquaculture vessel in a wave basin. Two wave directions (beam and head wave) and two filling levels (81.5% and 47.4%) are taken into account. The time-domain and frequency-domain characteristics of the sloshing response under the linear regular wave and extreme operational sea state were investigated using regular wave tests and irregular wave tests, respectively. The sloshing mechanism in the aquaculture tanks is complicated, due to the coupling effect between external waves, ship motion, and internal sloshing. In linear regular waves, the wave frequency mode dominates the sloshing response, which is larger under beam wave conditions than under head wave conditions and larger under half load conditions than full load conditions. The irregular wave test results confirmed the regular wave test conclusions, but the sloshing response has stronger nonlinearity, higher natural modes appeared, and the amplitude of the higher natural modes is also relatively larger. Full article
(This article belongs to the Special Issue New Techniques and Equipment in Large Offshore Aquaculture Platform)
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<p>Side view of the aquaculture vessel.</p> Full article ">Figure 2
<p>Transverse section of the aquaculture vessel at a standard aquaculture tank.</p> Full article ">Figure 3
<p>Aquaculture vessel model in the basin.</p> Full article ">Figure 4
<p>Aquaculture tank model.</p> Full article ">Figure 5
<p>Arrangement of the camera and all the sensors.</p> Full article ">Figure 6
<p>Arrangement of WG1–WG3 at the hull stern.</p> Full article ">Figure 7
<p>Sloshing time history and response spectrum under full load and regular beam wave condition. (<b>a</b>) Sloshing time history; (<b>b</b>) Response spectrum.</p> Full article ">Figure 8
<p>Sloshing RAOs under full load and beam wave condition. (<b>a</b>) Wave frequency mode; (<b>b</b>) First natural mode.</p> Full article ">Figure 9
<p>Sloshing response and response spectrum under half load and regular beam wave condition. (<b>a</b>) Sloshing time history; (<b>b</b>) Response spectrum.</p> Full article ">Figure 10
<p>Sloshing RAO under half load and beam wave condition. (<b>a</b>) Wave frequency mode; (<b>b</b>) First natural mode.</p> Full article ">Figure 11
<p>Sloshing response at different locations under full load condition. (<b>a</b>) Different longitudinal positions; (<b>b</b>) Different transverse positions.</p> Full article ">Figure 12
<p>Sloshing response at different locations within the tank.</p> Full article ">Figure 13
<p>Sloshing response and response spectrum under full load and regular head wave condition. (<b>a</b>) Sloshing response; (<b>b</b>) Response spectrum.</p> Full article ">Figure 14
<p>Sloshing response and response spectrum under half load and regular head wave condition. (<b>a</b>) Sloshing response; (<b>b</b>) Response spectrum.</p> Full article ">Figure 15
<p>Wave frequency sloshing RAO under head wave condition. (<b>a</b>) Full load; (<b>b</b>) Half load.</p> Full article ">Figure 16
<p>Sloshing response and response spectrum under full load and irregular beam wave condition. (<b>a</b>) Sloshing response; (<b>b</b>) Response spectrum.</p> Full article ">Figure 17
<p>Free surface of tank No.1. Red line: transient free surface at the tank wall.</p> Full article ">Figure 18
<p>Upwelling process at the walkway connection of tank No.2. The black line is the end of the walkway and the red line is the transient free surface at the tank wall. The free surface begins at the design waterline (<b>I</b>), gradually rises to the top of the walkway (<b>II</b>–<b>III</b>), further causing a localized upsurging swell at the walkway connection (<b>IV</b>–<b>V</b>), and then stays briefly on the walkway (<b>VI</b>–<b>VII</b>) and finally begins to fall (<b>VIII</b>–<b>IX</b>).</p> Full article ">Figure 19
<p>Sloshing response and response spectrum under half load and irregular beam irregular wave condition. (<b>a</b>) Sloshing response; (<b>b</b>) Response spectrum.</p> Full article ">Figure 20
<p>Typical evolution of the free surface under half load and beam wave condition. (<b>a</b>) First natural mode; (<b>b</b>) Second natural mode; (<b>c</b>) 3D standing waves. Red lines: the still water line; Yellow lines: the transient free surface at the tank wall.</p> Full article ">Figure 21
<p>Sloshing response and response spectrum under full load and irregular head irregular wave condition. (<b>a</b>) Sloshing response; (<b>b</b>) Response spectrum.</p> Full article ">Figure 22
<p>Sloshing response and response spectrum under half load and irregular head wave condition. (<b>a</b>) Sloshing response; (<b>b</b>) Response spectrum.</p> Full article ">Figure 23
<p>Typical free surface shape under head wave condition. (<b>a</b>) Full load; (<b>b</b>) Half load.</p> Full article ">
13 pages, 2116 KiB  
Article
Identification of Large Yellow Croaker under Variable Conditions Based on the Cycle Generative Adversarial Network and Transfer Learning
by Shijing Liu, Cheng Qian, Xueying Tu, Haojun Zheng, Lin Zhu, Huang Liu and Jun Chen
J. Mar. Sci. Eng. 2023, 11(7), 1461; https://doi.org/10.3390/jmse11071461 - 22 Jul 2023
Cited by 1 | Viewed by 1265
Abstract
Variable-condition fish recognition is a type of cross-scene and cross-camera fish re-identification (re-ID) technology. Due to the difference in the domain distribution of fish images collected under different culture conditions, the available training data cannot be effectively used for the new identification method. [...] Read more.
Variable-condition fish recognition is a type of cross-scene and cross-camera fish re-identification (re-ID) technology. Due to the difference in the domain distribution of fish images collected under different culture conditions, the available training data cannot be effectively used for the new identification method. To solve these problems, we proposed a new method for identifying large yellow croaker based on the CycleGAN (cycle generative adversarial network) and transfer learning. This method constructs source sample sets and target sample sets by acquiring large yellow croaker images in controllable scenes and actual farming conditions, respectively. The CycleGAN was used as the basic framework for image transformation from the source domain to the target domain to realize data amplification in the target domain. In particular, IDF (identity foreground loss) was used to optimize identity loss judgment criteria, and MMD (maximum mean discrepancy) was used to narrow the distribution between the source domain and target domain. Finally, transfer learning was carried out with the expanded samples to realize the identification of large yellow croaker under varying conditions. The experimental results showed that the proposed method achieved good identification results in both the controlled scene and the actual culture scene, with an average recognition accuracy of 96.9% and 94%, respectively. These provide effective technical support for the next steps in fish behavior tracking and phenotype measurement. Full article
(This article belongs to the Special Issue New Techniques and Equipment in Large Offshore Aquaculture Platform)
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<p>The framework for our method. There were two components of our method (i.e., a data transfer layer and a knowledge transfer layer). The data transfer part was mainly used to achieve sample expansion in the target domain, including CycleGAN, IDF, and maximum mean discrepancy. CycleGAN was mainly used to transfer images from the source domain to the target domain. IDF restricts CycleGAN to retaining fish identity information during the transfer process. The maximum mean discrepancy was used to narrow the distribution between the source and destination domains during transmission. Knowledge transfer was mainly used to improve the ability of the model to recognize the characteristics of fish, and the amplified data were mainly used to increase the effect of the transfer model on knowledge transfer.</p> Full article ">Figure 2
<p>Source domain and target domain ablation test images. From left to right: input, CycleGAN, CycleGAN+ maximum mean discrepancy, CycleGAN+ foreground mask loss, CycleGAN+ maximum mean discrepancy + foreground mask loss (our complete method).</p> Full article ">Figure 2 Cont.
<p>Source domain and target domain ablation test images. From left to right: input, CycleGAN, CycleGAN+ maximum mean discrepancy, CycleGAN+ foreground mask loss, CycleGAN+ maximum mean discrepancy + foreground mask loss (our complete method).</p> Full article ">
21 pages, 10392 KiB  
Article
Hydrodynamic Response Analysis of a Fixed Aquaculture Platform with a Horizontal Cylindrical Cage in Combined Waves and Currents
by Kanmin Shen, Chunwei Bi, Zhenqiang Jiang, Shouan Guo and Bin Wang
J. Mar. Sci. Eng. 2023, 11(7), 1413; https://doi.org/10.3390/jmse11071413 - 14 Jul 2023
Cited by 1 | Viewed by 1203
Abstract
Biofouling on net cages adversely affects structural safety and the growth of aquacultural fish. Therefore, a novel fixed aquaculture platform with a rotatable horizontal cylindrical cage is proposed in this study, which is convenient for the cleaning of biofouling. Based on ANSYS, the [...] Read more.
Biofouling on net cages adversely affects structural safety and the growth of aquacultural fish. Therefore, a novel fixed aquaculture platform with a rotatable horizontal cylindrical cage is proposed in this study, which is convenient for the cleaning of biofouling. Based on ANSYS, the numerical model of the fixed aquaculture platform was established. The response results of the strain, acceleration, and displacement of the structure under the combined action of waves and currents at three typical attack angles were calculated. The effects of water depth and cage rotation on the hydrodynamic response of the structure are discussed. The results show that the strain, acceleration, and displacement of the cage increase with the increase in wave height; however, the change with the wave period is not obvious. The direction perpendicular to the long axis of the cage is the most unfavorable load direction. The acceleration of each position increases with the increase in water depth; however, the strain response has the opposite trend. When the rotation constraint of the horizontal cylindrical cage is released, the acceleration of the cage is larger than that when the cage is fixed. The rotation of the cage has a tiny effect on the structural strain and load acting on the structure. Full article
(This article belongs to the Special Issue New Techniques and Equipment in Large Offshore Aquaculture Platform)
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Figure 1

Figure 1
<p>Sketch of the fixed aquaculture platform with a horizontal cylindrical cage.</p> Full article ">Figure 2
<p>Three-view drawing of the fixed aquaculture platform. (<b>a</b>). Main view. (<b>b</b>). Lateral view. (<b>c</b>). Top view.</p> Full article ">Figure 3
<p>Finite element model of the fixed aquaculture platform with a horizontal cylindrical cage.</p> Full article ">Figure 4
<p>Experimental diagram of the monopile net cage and the numerical model. (<b>a</b>). The experimental diagram. (<b>b</b>). The numerical model.</p> Full article ">Figure 5
<p>The force acting on the model under current load.</p> Full article ">Figure 6
<p>The force acting on the model under wave load.</p> Full article ">Figure 7
<p>The measuring points of strain, acceleration, and displacement.</p> Full article ">Figure 8
<p>Strain curves with different wave heights (<span class="html-italic">T</span> = 12.4 s). (<b>a</b>). Strain curve of S1. (<b>b</b>). Strain curve of S3. (<b>c</b>). Strain curve of S13. (<b>d</b>). Strain curve of S15.</p> Full article ">Figure 9
<p>Strain curves with different wave periods (H = 3m). (<b>a</b>). Strain curve of S1. (<b>b</b>). Strain curve of S3. (<b>c</b>). Strain curve of S13. (<b>d</b>). Strain curve of S15.</p> Full article ">Figure 10
<p>Strain peak of measuring points S1, S2, and S3 with a 0° attack angle. (<b>a</b>). The peak value varies with wave height. (<b>b</b>). The peak value varies with wave period.</p> Full article ">Figure 11
<p>Time history curves of strain for the measuring points (<span class="html-italic">H</span> = 3 m, <span class="html-italic">T</span> = 12.4 s). (<b>a</b>). S1, S2, and S3. (<b>b</b>). S13, S14, and S15.</p> Full article ">Figure 12
<p>Comparison of strain at different attack angles (<span class="html-italic">H</span> = 3 m, <span class="html-italic">T</span> = 12.4 s). (<b>a</b>). Comparison of strain curves at S1, S2, and S3 at attack angles of 45° (<b>left</b>) and 90° (<b>right</b>). (<b>b</b>). Comparison of strain curves at S13, S14, and S15 at attack angles of 45° (<b>left</b>) and 90° (<b>right</b>).</p> Full article ">Figure 13
<p>Time history curves of acceleration at J1, J4, J7, J10, and J13 (<span class="html-italic">H</span> = 3 m, <span class="html-italic">T</span> = 12.4 s).</p> Full article ">Figure 14
<p>Comparison of peak acceleration at measuring points J1, J2, and J3. (<b>a</b>). Peak acceleration when <span class="html-italic">T</span> = 12.4 s. (<b>b</b>). Peak acceleration when <span class="html-italic">H</span> = 3 m.</p> Full article ">Figure 15
<p>Comparison of acceleration frequency domain responses at J1, J3, J13, and J15. (<b>a</b>). Measuring points J1 and J3. (<b>b</b>). Measuring points J13 and J15.</p> Full article ">Figure 16
<p>Time history curves of acceleration for J1 and J3 at different attack angles (<span class="html-italic">H</span> = 3 m, <span class="html-italic">T</span> = 12.4 s). (<b>a</b>). Comparison of time history curves for J1 at attack angles of 45° (<b>left</b>) and 90° (<b>right</b>). (<b>b</b>). Comparison of time history curves for J3 at attack angles of 45° (<b>left</b>) and 90° (<b>right</b>).</p> Full article ">Figure 17
<p>Comparison of displacement time history curves. (<b>a</b>). Measuring points U1, U13, and U17. (<b>b</b>). Measuring points U16, U20, and U21.</p> Full article ">Figure 18
<p>Comparison of peak displacement values with different wave periods and wave heights. (<b>a</b>). <span class="html-italic">H</span> = 3 m. (<b>b</b>). <span class="html-italic">T</span> = 12.4 s.</p> Full article ">Figure 19
<p>Curves of displacement for U1 and U3 under different attack angles (<span class="html-italic">H</span> = 3 m, <span class="html-italic">T</span> = 12.4 s). (<b>a</b>). Comparison of time history curves of U1 at attack angles of 45° (<b>left</b>) and 90° (<b>right</b>). (<b>b</b>). Comparison of time history curves of U3 at attack angles of 45° (<b>left</b>) and 90° (<b>right</b>).</p> Full article ">Figure 20
<p>Displacement peak curves of measuring points under different attack angles. (<b>a</b>). U1, U2, and U3. (<b>b</b>). U13, U14, and U15. (<b>c</b>). U13, U14, and U15.</p> Full article ">Figure 21
<p>Acceleration peak curves of measuring points under different attack angles. (<b>a</b>). Measuring points J1, J4, J7, J10, and J13. (<b>b</b>). Measuring points J3, J6, J9, J12, and J15. (<b>c</b>). Measuring points J16, J17, J18, and J19. (<b>d</b>). Measuring points J16, J20, and J21.</p> Full article ">Figure 22
<p>Strain peak curves of measuring points under different attack angles. (<b>a</b>). Measuring points S1, S4, S7, S10, and S13. (<b>b</b>). Measuring points S3, S6, S9, S12, and S15. (<b>c</b>). Measuring points S13, S14, and S15. (<b>d</b>). Measuring points S16, S17, S18, S19, and S24.</p> Full article ">Figure 23
<p>Comparison curves of peak acceleration at different water levels. (<b>a</b>). Measuring points J3, J6, J9, J12, and J15. (<b>b</b>). Measuring points J16, J17, J18, J19, and J24.</p> Full article ">Figure 24
<p>Frequency spectrum curves for J16.</p> Full article ">Figure 25
<p>Comparison curves of peak displacement in relation to height under different water levels. (<b>a</b>). Measuring points U1, U4, U7, U10, and U13. (<b>b</b>). Measuring points U3, U6, U9, U12, and U15.</p> Full article ">Figure 26
<p>Comparison curves of peak strain response in relation to longitudinal measuring points. (<b>a</b>). Measuring points S1, S2, and S3. (<b>b</b>). Measuring points S7, S8, and S9. (<b>c</b>). Measuring points S13, S14, and S15. (<b>d</b>). Measuring points S16, S17, S18, S19, and S24.</p> Full article ">Figure 27
<p>Comparison curve of peak acceleration response.</p> Full article ">Figure 28
<p>Acceleration frequency domain curve.</p> Full article ">Figure 29
<p>Comparison of peak displacement response between rotatable and fixed net cages.</p> Full article ">Figure 30
<p>Comparison curve of peak strain response.</p> Full article ">Figure 31
<p>Comparison of force curves acting on the cage. (<b>a</b>). The force component along the X direction. (<b>b</b>). The force component along the Y direction. (<b>c</b>). The force component along the Z direction.</p> Full article ">
14 pages, 1090 KiB  
Article
Hydrodynamic Characteristic Analysis and NSGA-II Optimization of a Vacuum Fish Pump
by Yang Hong, Ye Zhu, Chenglin Zhang, Meng Yang and Tao Jiang
J. Mar. Sci. Eng. 2023, 11(7), 1361; https://doi.org/10.3390/jmse11071361 - 4 Jul 2023
Viewed by 1216
Abstract
The fish pump is an important piece of power equipment for aquaculture, especially for deep-sea engineering vessels and cage culture. Fish pump research focuses on reducing fish body damage, improving survival rates, and increasing fish lifting efficiency. The research object in this paper [...] Read more.
The fish pump is an important piece of power equipment for aquaculture, especially for deep-sea engineering vessels and cage culture. Fish pump research focuses on reducing fish body damage, improving survival rates, and increasing fish lifting efficiency. The research object in this paper is a new type of vacuum fish pump, with the aim of improving the hydraulic performance of the vacuum fish pump and reducing the damage to the fish body. The dependent variables include the dynamic change process of the flow state and flow field under diachronic conditions, the fluid simulation analysis of the vacuum pump body and the flow channel structure, the inlet flow rate of the fish pump, the negative pressure of the pipeline, and the impact force of the water flow on the inner wall of the tank. The independent variables include the operating conditions of the pump body and the fish pump. The Latin hypercube sampling method is used to extract 167 sets of calculation models for the independent variables, and multi-objective optimization is performed based on the NSGA-II algorithm for the hydrodynamic performance of the fish pump. On the basis of ensuring the fish body damage rate, the structural parameters of the vacuum fish pump with the optimal hydrodynamic performance under 167 sets of parameter values were obtained. The optimized parameters were then entered into the solver again, and the results showed that, in the optimal structural parameters under certain conditions, the direction of the incident water flow in the vacuum fish pump tank is close to the upper end of the tank body, which will reduce the speed of the fish-water mixed flow when entering the tank, thereby reducing the collision damage to the fish body. Currently, the water flow velocity at the water inlet is about 2.5 m/s, and the negative pressure value distribution gradient between the tank body and the water inlet pipeline is quite consistent, which can achieve good fish suction and fish lifting effects. Full article
(This article belongs to the Special Issue New Techniques and Equipment in Large Offshore Aquaculture Platform)
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Figure 1

Figure 1
<p>Design scheme of a vacuum fish pump.</p> Full article ">Figure 2
<p>Experimental setup and scheme.</p> Full article ">Figure 3
<p>Flow field analysis finite element model.</p> Full article ">Figure 4
<p>Model mesh division.</p> Full article ">Figure 5
<p>Comparison of experimental values and CFD simulation values.</p> Full article ">Figure 6
<p>Instantaneous flow state distribution inside the fish pump. (<b>a</b>) 1 s (<b>b</b>) 3 s (<b>c</b>) 5 s.</p> Full article ">Figure 7
<p>Instantaneous flow velocity distribution inside the fish pump. (<b>a</b>) 1 s (<b>b</b>) 3 s (<b>c</b>) 5 s.</p> Full article ">Figure 8
<p>Instantaneous pressure distribution inside the fish pump. (<b>a</b>) 1 s (<b>b</b>) 3 s (<b>c</b>) 5 s.</p> Full article ">Figure 9
<p>The instantaneous flow state and flow field distribution inside the optimized fish pump. (<b>a</b>) Fluid distribution (<b>b</b>) Velocity distribution (<b>c</b>) pressure distribution.</p> Full article ">
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