Simulated Shipping Research Articles

A-319 Capillary Blood Collection for Egg-cellent Reproductive Insights

Abstract Background In the United States, an estimated 13% of women between the ages of 15 and 49 years, experience impaired reproductive potential (fecundity). Assessment of fecundity typically requires an in-person physician visit and venous blood collection to evaluate hormone levels and identify potential reproductive health conditions. Exploring less invasive, at-home sampling options could offer couples insights into their personal reproductive abilities, providing peace of mind and aiding in family planning. Methods Capillary blood, obtained using the Tasso®+ capillary blood collection device coupled with a BD Microtainer® serum tube, was utilized for measurements of analytes associated with female fecundity (follicle stimulating hormone, luteinizing hormone, estradiol, progesterone, anti-Müllerian hormone, and prolactin). These are all FDA-approved assays measured on Roche cobas® e801, assay parameters remained unchanged. Professional and self-collected capillary serum samples were acquired in parallel with the standard venous serum samples, to compare clinical equivalency of the capillary sampling option. In addition to isothermal stability studies at room temperature (20-25°C) and refrigerated (2-8°C) conditions, shipping excursion studies based on ISTA 7D recommendations were performed using a novel packaging solution to control sample temperature and thus, specimen integrity. Studies to assess matrix equivalency, linearity, and imprecision of this alternate sample type were also performed to demonstrate analytical performance. Results Method comparison of venous and professional collection of capillary samples demonstrated correlations (R) ≥ 0.9720 with slopes ranging from 0.919 to 0.971 and mean biases within ±8.2% for all analytes (n = 19-79). Self-collected capillary serum samples demonstrated correlations (R) ≥ 0.9827 with slopes that ranged from 0.841 to 0.985 and mean biases within ±10.7% (n = 16-26). Isothermal stability was demonstrated for each analyte up to 7 days at room temperature and refrigerated conditions. Simulated shipping excursion results were found to be acceptable for both winter and summer conditions with mean biases for each analyte within ±10.2% and ±19.2%, respectively. Matrix equivalency, linearity, and imprecision results adhered to current CLIA acceptance limits for each respective measurement. Conclusions The analytical and stability results affirm the feasibility of collecting capillary serum in a patient-centric manner, providing women with relevant information about their reproductive health.

Simulation of Ship Berthing Operation at Luojing Container Terminal Under Extreme Sea Conditions

The Luojing Port Area of the Port of Shanghai, specifically the coal terminal and ore terminal, used to be the main port area for coal and ore bulk cargo transportation services in the Port of Shanghai.To enhance the container handling capacity at Shanghai port, this study conducted a series of simulation tests at Luojing Container Terminal. The tests were designed according to the terminal's specifications, taking into account the limit berthing wind direction and wind speed (levels 6 and 7). This study selected an appropriate representative ship type for the comprehensive simulation tests, and it thoroughly tested the berthing limits under various extreme conditions using an advanced navigation simulator. The experiment obtained the motion parameters and trajectory of the simulated ship. Based on these results, this study analyzed and evaluated the safety of the rotary waters and berthing operations, ensuring they met the safety assessment requirements for wharf engineering. The study examined the berthing time window, berthing mode, boundary conditions, and safety guarantee measures under extreme sea conditions at Luojing Container Terminal. Finally, By analyzing the berthing simulation trajectory diagrams, tugboat usage, and vessel maneuvering data under the eight extreme berthing conditions, this study formulated a safe berthing plan for ships.

Capture of Sulfur Dioxide in Ship Exhaust Gas by Tertiary Amine Absorbents

AbstractSulfur dioxide (SO2) emissions from ship exhausts pose serious health and environmental concerns. Herein, the SO2 absorption and desorption characteristics of tertiary amines with different functional groups were explored under simulated ship exhaust gas conditions. Tertiary amines with electron‐donating groups had superior absorption performance to those with electron‐withdrawing groups, and highly polar absorbents exhibited enhanced SO2 absorption loading. Dimethylaniline (DMA) showed excellent desorption performance, outperforming other absorbents (amino acids, ionic liquids, and deep eutectic solvents) in terms of cyclic capacity. Thus, tertiary amines, especially DMA, can be potentially used for the prevention of SO2 emissions from ship exhausts and other desulfurization applications.

Ship Ballast Water System Fault Diagnosis Method Based on Multi-Feature Fusion Graph Convolution

To tackle the issues of limited fault data, inadequate information availability, and subpar fault diagnosis within the realm of ship ballast water system condition monitoring, this paper presents a novel fault diagnosis methodology known as the Probabilistic Similarity and Linear Similarity-based Graph Convolutional Neural Network (PCGCN) model. PCGCN initially converts the ship’s ballast water system dataset into two distinct graph structures: a probabilistic topology graph and a correlation topology graph. It delves into data similarity by employing T-SNE for probabilistic similarity and Pearson’s correlation coefficient for linear similarity to establish the inter-sample neighbor relationships. Subsequently, an early fusion of these two graph structures is conducted to extract more profound multi-scale feature information. Following this step, the graph convolutional neural network (GCN) is introduced to amalgamate the feature information from neighboring nodes in addition to its inherent features. This is aimed at enhancing the available information for the classification task and addressing the issues of limited fault data and inadequate label information. In conclusion, we employ a simulated ship fault dataset for testing experiments, and the PCGCN model demonstrates superior classification accuracy, reaching 97.49%, outperforming traditional diagnostic methods. These experimental outcomes underscore the applicability of the model introduced in this study to the realm of ship ballast water system fault diagnosis, even under challenging conditions characterized by limited sample sizes and insufficient labeling information.

Testing for biofilm release as a function of simulated ship speed using a calibrated water jet device

Biofouling release coatings (BRCs) have received attention for their potential to limit the negative impacts of biofouling on marine shipping. The calibrated water jet (CWJ, patent # US 8,984,958 B1) can be used to study the effectiveness of BRCs as a function of ship speed. Using a balance of force and linear momentum, we examined the theory and application of the CWJ for simulating the effect of ship speed on biofilm release for surfaces fouled under (1) laboratory and (2) natural conditions. Greater fouling release corresponded with an increase in CWJ pressure and, therefore, simulated ship speed for the surfaces coated with HullKote. The effectiveness of the CWJ was further confirmed for biofilm release from glass fouled naturally by submersion in flow-through seawater. A scaling analysis confirms that the results of these small-scale experiments are applicable to larger-scale biofouling release from ship hulls. This study is the first to utilize the pressure of a CWJ to quantify biofouling release as a function of simulated ship speed.

Assessing mouse, trackball, touchscreen and leap motion in ship vibration conditions: A comparison of task performance, upper limb muscle activity and perceived fatigue and usability

A variety of interaction devices are increasingly used for human-computer interaction (HCI) tasks in ship vibration conditions, but have seldom been well assessed. This study aimed to examine task performance, upper limb muscle activity, and perceived fatigue and device usability for four typical interaction devices (i.e., mouse, trackball, touchscreen, and Leap Motion) under simulated ship vibration conditions. A two-factor within-subjects design was employed in this study, where participants performed basic HCI tasks with the four interaction devices under three conditions (i.e., static condition, and low and high vibration conditions). The results showed that vibration condition significantly reduced task performance, especially for Leap Motion. Differences in task performance, upper arm and shoulder muscle activities, perceived fatigue and device usability were found among interaction devices. Mouse and touchscreen achieved the best task performance, compared with trackball and Leap Motion, while both touchscreen and Leap Motion achieved larger upper limb muscle activities, compared with trackball and mouse. Relevance to industryThe findings provide important implications for the use and configuration of interaction devices, and for the development of prevention interventions for risks of musculoskeletal disorders in using interaction devices under ship vibration conditions.

Enhancing Automated Loading and Unloading of Ship Unloaders through Dynamic 3D Coordinate System with Deep Learning

This paper proposes a deep learning approach for accurate pose estimation in ship unloaders, improving grasping accuracy by reconstructing 3D coordinates. A convolutional neural network optimizes depth map prediction from RGB images, further enhanced by a conditional generative adversarial network to refine quality. Evaluation of simulated ship unloading tasks showed over 90% grasping success rate, outperforming baseline methods. This research offers valuable insights into advanced visual perception and deep learning for next-generation automated cargo handling.

Efficacy evaluation of adaptive collision avoidance systems for autonomous maritime surface ships based on target ships’ maneuvering behaviors

ABSTRACT Ongoing advancements in collision avoidance systems (CAS) for autonomous ships, built on diverse algorithms, have significantly enhanced performance. These systems, underpinned by a range of sophisticated algorithms, demonstrate increased accuracy in detecting potential collisions, quicker response times, and more reliable path prediction and avoidance maneuvers. Nevertheless, a prevalent trend in these systems is the insufficient consideration of the broader behavioral context of maritime navigation, especially for manned ships. The objective of this research is to present a methodology that can improve the capabilities of CAS in autonomous ships based on target ships’ maneuvering behaviors. This methodology will enable autonomous ships to respond adaptively to the actions of target ships, particularly manned ships, while also reacting to immediate environmental stimuli. The ultimate goal is to enhance the safety of navigation. This research utilizes information derived from simulated ship handling training, focusing on feature extraction within steering, spatial, and temporal elements. The tests prioritize the “Average Distance Deviation” metric for designing test scenarios. Simulation-based evaluation tests feature a velocity obstacle algorithm that discerns CAS action in single-ship crossing situations. The research reveals the potential of integrating behavioral dynamics into CAS, which enhances safety in autonomous maritime navigation and fosters a more integrated approach, where autonomous and manned ships coexist synergistically in shared environments.

Experimental Testing of CO2 Specifications Under Simulated Ship Transport Conditions

Experimental Testing of CO2 Specifications Under Simulated Ship Transport Conditions

Research Method for Ship Engine Fault Diagnosis Based on Multi-Head Graph Attention Feature Fusion

At present, there are problems such as low fault data, insufficient labeling information, and poor fault diagnosis in the field of ship engine diagnosis. To address the above problems, this paper proposes a fault diagnosis method based on probabilistic similarity and rank-order similarity of multi-head graph attention neural networks (MPGANN) models. Firstly, the ship engine dataset is used to explore the similarity between the data using the probabilistic similarity of T_SNE and the rank order similarity of Spearman’s correlation coefficient to define the neighbor relationship between the samples, and then the appropriate weights are selected for the early fusion of the two graph structures to fuse the feature information of the two scales. Finally, the graph attention neural networks (GANN) incorporating the multi-head attention mechanism are utilized to complete the fault diagnosis. In this paper, comparative experiments such as graph construction and algorithm performance are carried out based on the simulated ship engine dataset, and the experimental results show that the MPGANN outperforms the comparative methods in terms of accuracy, F1 score, and total elapsed time, with an accuracy rate of 97.58%. The experimental results show that the model proposed in this paper can still fulfill the ship engine fault diagnosis task well under unfavorable conditions such as small samples and insufficient label information, which is of practical significance in the field of intelligent ship cabins and fault diagnosis.

The Development of a Steering Angle Estimation Model for Evaluation of Simulated Ship Handling Training for Remote Operators of Autonomous Ships

Through technological development, ships are being automated, reducing the number of human operators. Accordingly, the responsibility of humans becomes more significant, making a single operator’s proficiency count. Simulated ship-handling training evaluates trainees’ proficiency using specific criteria to verify proficiency. However, the present criteria are confined to training scenarios, and it is hard to determine whether trainees finally achieved expert-like ability. This research conducted probabilistic estimation on experts’ average steering angles. The paper contains the corresponding explanations for each step of the research methods, from the data preprocessing step to the probabilistic steering angle estimation. The research findings include the experts’ trendline of average steering angles and the sample trainee’s evaluation results.

Kinematic motion models based vessel state estimation to support advanced ship predictors

Advanced ship predictors can generally be considered as a vital part of the decision-making process of autonomous ships in the future, where the information on vessel maneuvering behavior can be used as the source of information to estimate current vessel motions and predict future behavior precisely. As a result, the navigation safety of autonomous vessels can be improved. In this paper, vessel maneuvering behavior consists of continuous-time system states of two kinematic motion models—the Curvilinear Motion Model (CMM) and Constant Turn Rate & Acceleration (CTRA) Model. Two state estimation algorithms—the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are implemented on these two models with certain modifications so that they can be compatible with discrete-time measurements. Four scenarios, created by combining different models and algorithms, are implemented using simulated ship maneuvering data from a bridge simulator. These scenarios are then verified through the proposed stability and consistency tests. The simulation results show that the EKF tends to be unstable combined with the CMM. The estimates from the other three scenarios can generally be considered more stable and consistent, unless sudden actions or variations in vessel heading occurred during the simulation. The CTRA is also proven to be more robust compared to the CMM. As a result, a suitable combination of mathematical models and estimation filters can be considered to support advanced ship predictors in future ship navigation.

Growth performance, feed utilization, and resistance to acute crowding of post‐metamorphic stage black sea bass Centropristis striata reared at high stocking densities in a pilot recirculating nursery

AbstractA reliable and cost‐effective source of fingerlings is essential for the successful commercial aquaculture of black sea bass. This study aimed to determine the effects of nursery tank stocking densities on the growth performance, feed efficiency, survival, tolerance to shipping, and biochemical quality of juvenile black sea bass. In two experiments, different stocking densities were compared for black sea bass at different stages of development. In Experiment 1, intermediate stage juveniles (1.27 g mean wt) were stocked at densities of 4.5 and 6.5 fish per liter of tank volume. Growth performance and biochemical quality were similar between the two density treatments. In Experiment 2, early post‐metamorphic stage juveniles (mean wt. = 0.524 g) were compared using the same stocking densities. While the fish reared at 4.5 fish/L were slightly larger, there were no significant differences in growth performance or biochemical quality between the density treatments. After both experiments, the fingerlings from each density treatment showed high survival rates under acute crowding and simulated shipping conditions. The study demonstrates that high NT densities of 4.5 and 6.5 fish/L did not negatively impact the growth performance and quality of black sea bass fingerlings, providing transport‐ready fingerlings in a relatively short period.

Multi-Source HRRP Target Fusion Recognition Based on Time-Step Correlation

Aiming at the limitations of a single High Resolution Range Profile (HRRP) in recognition, this paper proposes a Time step Correlation-based Feature Fusion (TCFF) method. This method calculates the covariance matrix at the time step of the two features extracted by the two channels, and assigns different weights to the time step according to the strength of the covariance correlation for feature fusion. The experimental results on the simulated ship target HRRP dataset show that the feature fusion method can achieve better recognition performance than the single channel model. Compared with simple feature fusion such as element addition and element contacting, it can also achieve better recognition results.

Skin discoloration and softening are the primary factors impacting postharvest quality of hot water-treated mango (Mangifera indica L.)

About 85 to 90% of mangos imported to the United States from Mexico are hot water-treated (HWT). Mango chilling injury (CI) disorder limits prolonged low-temperature storage and affects the ability of shippers to transport mangos over long distances. Imported mangos display various CI symptoms that influence fruit postharvest quality, such as grayish, scald-like skin discoloration, lenticel spotting, skin pitting, loss of flesh color and flesh browning. There is limited information available on the interaction between harvest maturity, temperature and duration of exposure and their effects on postharvest life. We examined the relationship between cultivar, maturity stage at harvest, shipping temperature, and shipping duration of HWT mangos; the onset and severity of CI incidence, along with impact on consumer quality, and used the data to predict shipping-distribution postharvest life. Among the tested cultivars, ‘Tommy Atkins’ showed high incidence of skin damage (41%) after 10 days of cold storage, among all temperatures and maturity stages. Skin damage was the main barrier affecting postharvest quality. Within 24 days of simulated shipment, ‘Tommy Atkins’ showed a minimum of 11% incidence of flesh damage among temperatures and maturity stages. The onset of flesh damage occurred much later than skin damage and its incidence was low and manageable by selecting cultivar, harvest maturity and temperature during shipment to prolong postharvest life.To avoid CI during transportation and assure high consumer quality, HWT mangos should be transported at ≥ 10.0 to 12.5 °C. However, softening and decay incidence may become a problem during distribution at the destination. Therefore, improved packaging to protect soft mangos during handling at the receiving end and consumer educational programs are critical.

Wet scrubbing process with oxidation and reduction in series for removal of SO2 and NO from marine diesel engine exhaust

A novel wet scrubber comprising of an oxidation and a reducing section in series was used for the removal of SO2 and NOx from simulated ship emissions. Sodium chlorite (NaClO2) and sodium thiosulfate (Na2S2O3) were used for the oxidizing and reducing sections, respectively. Results showed that although increasing the pH of the chlorite oxidant led to less NO being oxidised, lower rates were achieved for both soluble nitrogen formation (chiefly in the form of nitrates) and reactant consumption per mole of pollutant removed. On the reduction side, it was shown that although the more well-known sulfite (Na2SO3) was about 15% more efficient in removing NO2 compared to thiosulfate, the latter had a very low reactant consumption rate, about 118 and 27 times less compared with sulfite and formaldehyde-stabilised sulfite. The formation of soluble nitrogen was also very low for thiosulfates (less than 0.18 mols formed per mole of NOx removed) or between 1.6 and 1.8 times less when compared with sulfites, as most of the pollutant was reduced to harmless N2. Although thiosulfates formed sulfur precipitates during reaction, this could be circumvented by operating at a high pH. A certain percentage of washwater from the scrubbers can also be discharged, with mixing from the oxidation and reduction sections for the adjustment of oxidation potential, within the allowable nitrate limits.

Identification of recurring patterns and repetitions in time-series, with application to pseudo-stochastic ship rolling

A scheme for capturing repeating portions in time-series is proposed. Such repetitions may be temporal or permanent. The scheme is applied for investigating repetitions in ship roll time simulation data, incurred by the use of discretized wave spectra. The presence of repetitions in the forcing can slant the ship motion response data collected via Monte Carlo simulations unless certain precautions are applied. The conventional check for repetitions in data series is based on the observation of high peaks in the autocorrelation function. However, as the autocorrelation reflects averaged resemblance between sets of successive data separated by a time interval, these peaks may not imply repetition in exact sense. By the proposed computational scheme can be elicited whether any given portion of the time-series reappears. The basic idea is the construction of low-dimensional vectorial representatives of short-time portions of the series and the check for similarity (to a level of tolerance) through application of a Euclidian distance metric to these vectors. The scheme is also useful for locating the end of the transient part in simulated ship roll motion data deriving from repeating pseudo-stochastic excitation.

Multi-omics reveals response mechanism of liver metabolism of hybrid sturgeon under ship noise stress

Underwater noise from ship engines can affect the metabolism and immune system of various fish species. Meanwhile, changes in the metabolic pathways in liver are important for fish to adapt to adverse environments. We used a combined multi-omics analysis to investigate the response mechanism of hybrid sturgeon to continuously played ship noise. A control group and a noise group (simulated ship noise: 12 h) were set up, and liver tissues were extracted for high-throughput transcriptome and metabolome sequencing. The results show that a total of 588 differentially expressed genes (DEGs) and 58 DEGs metabolites were detected. The joint analysis of transcriptome and metabolome showed that under noise stress, apoptosis and cell motility were intensified, DNA replication, RNA transcription and translation, and protein synthesis were inhibited, and lipid metabolism, nucleotide metabolism, and vitamin D3 metabolic pathways were also inhibited. Interestingly, the initiation of a partial immune responses ensured their normal immunity abilities. Moreover, material and energy requirements of the organism under noise stress were guaranteed by upregulation of carbohydrate and amino acid metabolic pathways.

Simultaneous removal of urea nitrogen and inorganic nitrogen from high-salinity wastewater by Halomonas sp. H36.

To treat high-salt urea wastewater by microbial hydrolysis, it is necessary to overcome the dual problems of incomplete removal of nitrogen (N) from mixed strains and inhibition of microbial activity by high salt (NaCl) concentrations. In this paper, the mechanism of NaCl tolerance of Halomonas sp. H36 was investigated. Using molecular biology and enzymatic methods, it was proven that the strain's N-removal enzymes (urease; ammonia monooxygenase, AMO; nitrite reductase, NIR; nitrate reductase, NAR) played a key role in the removal of N, and the N-removal pathway was clarified. For the strain used to treat simulated ship domestic sewage, the urea nitrogen (CO(NH2)2-N)-removal rate was 88.52%, the ammonia nitrogen (NH4+-N)-removal rate was 91.16%, the total nitrogen (TN)-removal rate was 90.25%, and nitrite nitrogen (NO2--N) and nitrate nitrogen (NO3--N) did not accumulate. It was proven for the first time that Halomonas sp. H36 has the function of simultaneous urea hydrolysis-nitrification-denitrification with urea as the initial substrate and can simultaneously remove urea nitrogen and inorganic nitrogen from high-salt urea wastewater.

Prediction of Stability during Walking at Simulated Ship's Rolling Motion Using Accelerometers.

Due to a ship’s extreme motion, there is a risk of injuries and accidents as people may become unbalanced and be injured or fall from the ship. Thus, individuals must adjust their movements when walking in an unstable environment to avoid falling or losing balance. A person’s ability to control their center of mass (COM) during lateral motion is critical to maintaining balance when walking. Dynamic balancing is also crucial to maintain stability while walking. The margin of stability (MOS) is used to define this dynamic balancing. This study aimed to develop a model for predicting balance control and stability in walking on ships by estimating the peak COM excursion and MOS variability using accelerometers. We recruited 30 healthy individuals for this study. During the experiment, participants walked for two minutes at self-selected speeds, and we used a computer-assisted rehabilitation environment (CAREN) system to simulate the roll motion. The proposed prediction models in this study successfully predicted the peak COM excursion and MOS variability. This study may be used to protect and save seafarers or passengers by assessing the risk of balance loss.