Starboard Research Articles

Framework for liner shipping connectivity data analytics and research

PurposeThis research proposes a framework to conceptualise the potential realm of data regarding shipping connectivity for application of data analytics which can be used to generate deeper insights with respect to the state of such linkages and potential areas for practical application.Design/methodology/approachThe study method involved comprehensive presentation of different perspectives of assessing shipping connectivity and levels of data contained within container shipping services and proposed potential application to analyse profitability, performance, competitiveness, risk and environmental impact.FindingsAdvances in capabilities to handle large volumes of data offer scope for an integrated approach which utilises all available data from various stakeholders in analyses of liner shipping connectivity. Research shows how different types of data contained in container shipping services are related and can be organised for application of data analytics.Research limitations/implicationsResearch implications are offered to shipping lines, port managers and operators and policymakers.Practical implicationsThis research presented a conceptual framework that captures the range of data involved in container shipping services and how data analytics can be practically applied in an integrated manner.Originality/valueThis paper is the first in literature to discuss in detail the different levels of data that reside within shipping services that constitute liner shipping connectivity for application of data analytics.

A multi-variable hybrid system for port container throughput deterministic and uncertain forecasting

Container transport is the most environmentally friendly and sustainable way to transport bulk cargo currently. The accurate prediction of container throughput is of great significance to the operation of the port. It is directly related to the healthy development of the entire logistics system, and also serves as a reference for decision makers in the construction of port infrastructure. However, the container throughput sequence contains the influence and restriction of a variety of superimposed factors, and has the characteristics of nonlinear and non-stationary. Traditional technology cannot effectively capture the changes of external related factors. To make up for the deficiencies, a multi-variable hybrid deterministic and uncertain forecasting framework based on improved preprocessing technique and swarm intelligence optimizer is constructed. It not only considers the impact of port hinterland economic indicators, port transport system and transport capacity factors, but also calculates the upper and lower limits of container throughput under different probabilities. The results and tests show that mean absolute percentage errors obtained by the presented forecast system are 1.1551%, 2.0936% and 2.5546% respectively, and the prediction interval coverage probability values of 1 can be achieved. The hybrid system is effective, stable and has reasonable running time, which can be widely popularized. And it is conducive to the orderly operation of transportation industry and the development of container port cities.

The design and energy saving effect of partial duct and unconventional pre-swirl fin and their combination system

The present work investigates two pre-swirl devices; partial duct and pre-swirl fins and their combination system. Commercial CFD code STARCCM+ is used to solve the Reynolds Averaged Navier-Stokes (RANS) equations for the ship flow problem. The influence of duct inclination and longitudinal distance from propeller plane on duct performance are investigated. An unconventional pre-swirl fin is proposed which could be suitable for both port and starboard. The fin is fixed at an angle of attack with respect to a horizontal plane as opposed to conventional angular position around the propeller shaft. A partial ducts consisting two half-ducts is investigated. Improvement in energy saving from 3.69% (duct acting on both sides) to 4.26% (duct acting on port side only) were recorded and the port duct has superior saving effect compared to the starboard duct. For improved saving effect, the port and starboard fins are mounted at opposite angles of attack, −10.45° and +10.45° respectively for the right hand propeller. The saving effect is further improved when the duct and pre-swirl fin are in combination with duct mounted on the port and the pre-swirl fin on the starboard. A validation study using this combination system is in close agreement with numerical method.

Effect of Star Board Rewards on Learning Motivation of 4-5 Year-Old Children: A Quantitative Experimental Study

This study investigates the impact of providing star board rewards on the learning motivation of children aged 4-5 years in Kindergarten Aisyiyah 09 Kranji Lamongan. Using a quantitative experimental approach with a typo one group pretest-posttest research design, the research was conducted in an experimental class comprising 13 students. A motivational questionnaire with 16 questions was used as the assessment instrument, and data collection occurred through pretest and posttest evaluations. The data were analyzed using paired sample t-test, revealing a significant increase in the average value of children's learning motivation by 35.1% from the pretest value to 44.1% in the posttest. Consequently, the findings indicate that giving star board rewards positively influences children's learning motivation (p < 0.05). The implications of this research contribute to our understanding of effective teaching strategies and provide valuable insights for educators and policymakers seeking to enhance children's learning motivation in early education settings. Highlight: The study examines the impact of star board rewards on learning motivation in young children. A quantitative experiment with a typo one group pretest-posttest design was used for data collection. Significant improvement in children's learning motivation was observed after the implementation of star board rewards. Keyword: Star Board Rewards, Learning Motivation, 4-5 Years Old, Kindergarten, Quantitative Experiment

Detection and cancellation method of impulse noise in gearbox non-stationary vibration signals—a case study of port ship unloader

This work proposes a signal preprocessing framework that cancels out impulse noise in the non-stationary gearbox vibration signals of a ship unloader, which reduce the interference of non-cyclic impulses on the damage detection. The algorithm involves three main steps: (i) Preliminary localization of impulse noise, including suppressing components related to normal machine operation, segmenting the signal, and localizing abnormal sub-segment signal based on higher-order statistical criteria. (ii) Detection of impulse boundaries. The influence of random impulse noise on the signal envelope spectrum is studied, and an impulse indicator is constructed to accurately detect the boundaries of impulse noise in abnormal sub-segment signals. (iii) Cancellation of impulse noise. Replace the identified random impulse with Gaussian white noise. Laboratory and industrial data confirm the excellent performance of the proposed algorithm. It can accurately identify and cancels out the non-cyclic impulse noise in the non-stationary gearbox vibration signal, which facilitates the subsequent damage detection.

A port and starboard fuzziness suppressed algorithm based on vector directivity zero

Conventional linear array based on acoustic pressure hydrophone can not distinguish the target signal from the port side, so it can not locate the target accurately. The vector hydrophone has natural directivity and can be resolved from starboard to starboard after being formed. However, in practical engineering, it is necessary to further improve the resolution of starboard to starboard because of noise or interference, etc. A new method of port and starboard suppression based on directivity of vector linear array is proposed. The simulation results show that the starboard suppression ratio of vector beam output is enhanced. Experimental data processing results show that this method can suppress the starboard ambiguity of vector beamforming and improve the starboard resolution of vector linear array. The processing results verify the feasibility of the algorithm, and the algorithm has certain application value.

META ANALYSIS THE EMISION OF GREEN PORT CITY : A LITERATURE REVIEW

ABSTRAK Air emission not only from waste or gas from chimney of factory or ships in port, but also from leaking gas of freon/chloro in air that can damaging the ozone. Our current understanding of the sources and increase in emissions of leaked refrigerant gases derives from two data sources: ground-based and airborne point measurements, or from reports of the quantity of products manufactured and purchased. Thus, port-related air pollutant emissions and their environmental impacts are not systematically reported. The solution to the environmental impacts of refrigerant gases would therefore pass by a gas which contains no chlorine no fluorine and does not reject any CO2 emissions in the atmosphere, in brief a green gas. The method used is expected to be able to map the center of the pollutant source.

484. ROBOTIC ASSISTED PRONE ESOPHAGECTOMY USING VERSIUS PLATFORM: A PROMISING APPROACH

Abstract Background Esophagectomy for cancer is a complex procedure with high morbidity. Minimally invasive approach offers the benefits of minimal access across the different fields required during this operation. Robotic surgery is becoming an integral part of modern practice. It holds the potential of more precision especially in narrow thoracic space. Currently, Many Robotic platforms exist. This video aims to demonstrate the feasibility of Robotic Mc Keown esophagectomy in the prone position using Versius platform. Methods 56 years old male patient presented with grade I dysphagia of 3 months duration. His complete assessment revealed an 11 cm long invasive non keratinizing squamous cell carcinoma located in the mid esophagus (cT3N1M0). Marked regressive course of the lesion occurred with neoadjuvant chemoradiation. Robotic assisted Mc Keown esophagectomy was performed in the prone position. Thoracic phase was performed with the robotic approach. Bed side units and Port placement is illustrated in the video. The abdominal phase was accomplished laparoscopically. Finally, the stomach was pulled up and the esophagogastric anastomosis was done in the neck. Results No intraoperative adverse events were encountered. The improved vision, dexterity and articulation of instruments facilitated the identification of the thoracic duct, ligation of azygos vein tributaries and dissection of the recurrent laryngeal lymph nodes. Instrument clash was insignificant due to patient’s wide chest cavity and optimal positioning of ports and bed side units improving ergonomics. The operative time of the thoracic phase was 310 min. Blood loss was 300 mL. The postoperative course was uneventful except for chest infection which improved on conservative measures. The length of stay was 18 days. Pathology revealed complete clinical response with 0/25 lymph node yield. Conclusion Robotic assisted esophagectomy is feasible and can be safely performed using Versius robotic platform. The enhanced visualization and articulation enabled meticulous dissection of the recurrent laryngeal lymph nodes. Patient positioning, optimal port and bed side units placement were keys to achieving minimal instrument clash. Further research is needed to confirm these findings and to compare the long-term oncological outcomes and cost effectiveness of this modality. Video links: https://we.tl/t-rpkTHvNlXI or https://drive.google.com/drive/folders/1tOsFPrEBUHBpVn8C9jF7BGoNxlYPuBjq?usp=sharing.

Perkembangan Pelabuhanan Merak Sebagai Pusat Perdagangan dan Pelayaran di Indonesia 1912-2009

The Merak crossing port route, which connects the islands of Java and Sumatra and is in the city of Cilegon in the province of Banten and is also the busiest crossing in Indonesia, serves as a traffic gateway. In 1912, Merak Harbor saw its first use. Merak Harbor was a spearheading ship port underlying 1912 by the Dutch East Indies government. Before becoming a national asset of Indonesia, the port served colonial political purposes when it was built. The presence of this port is a financial resource for Banten region which has an ocean transportation strategy heading with the point and objective of turning into "the pre-prominent port territory in Indonesia". The reason for this study is to figure out the idea of the improvement of the Merak port as an exchange and delivery focus. Heuristics, source criticism, interpretation, and historiography are the four stages of the method used to write this research. This study relied on books, documents, and journals related to the port of Merak as sources from literature studies that included technical historical analysis based on interpretations of historical facts. The consequences of the review show that the Port of Merak is as yet dynamic in trading merchandise abroad, the presence of the Merak Crossing Port likewise extensively affects the encompassing local area.

Native diversity buffers against severity of non-native tree invasions

Determining the drivers of non-native plant invasions is critical for managing native ecosystems and limiting the spread of invasive species1,2. Tree invasions in particular have been relatively overlooked, even though they have the potential to transform ecosystems and economies3,4. Here, leveraging global tree databases5–7, we explore how the phylogenetic and functional diversity of native tree communities, human pressure and the environment influence the establishment of non-native tree species and the subsequent invasion severity. We find that anthropogenic factors are key to predicting whether a location is invaded, but that invasion severity is underpinned by native diversity, with higher diversity predicting lower invasion severity. Temperature and precipitation emerge as strong predictors of invasion strategy, with non-native species invading successfully when they are similar to the native community in cold or dry extremes. Yet, despite the influence of these ecological forces in determining invasion strategy, we find evidence that these patterns can be obscured by human activity, with lower ecological signal in areas with higher proximity to shipping ports. Our global perspective of non-native tree invasion highlights that human drivers influence non-native tree presence, and that native phylogenetic and functional diversity have a critical role in the establishment and spread of subsequent invasions.

Fire Detection and Notification Method in Ship Areas Using Deep Learning and Computer Vision Approaches.

Fire incidents occurring onboard ships cause significant consequences that result in substantial effects. Fires on ships can have extensive and severe wide-ranging impacts on matters such as the safety of the crew, cargo, the environment, finances, reputation, etc. Therefore, timely detection of fires is essential for quick responses and powerful mitigation. The study in this research paper presents a fire detection technique based on YOLOv7 (You Only Look Once version 7), incorporating improved deep learning algorithms. The YOLOv7 architecture, with an improved E-ELAN (extended efficient layer aggregation network) as its backbone, serves as the basis of our fire detection system. Its enhanced feature fusion technique makes it superior to all its predecessors. To train the model, we collected 4622 images of various ship scenarios and performed data augmentation techniques such as rotation, horizontal and vertical flips, and scaling. Our model, through rigorous evaluation, showcases enhanced capabilities of fire recognition to improve maritime safety. The proposed strategy successfully achieves an accuracy of 93% in detecting fires to minimize catastrophic incidents. Objects having visual similarities to fire may lead to false prediction and detection by the model, but this can be controlled by expanding the dataset. However, our model can be utilized as a real-time fire detector in challenging environments and for small-object detection. Advancements in deep learning models hold the potential to enhance safety measures, and our proposed model in this paper exhibits this potential. Experimental results proved that the proposed method can be used successfully for the protection of ships and in monitoring fires in ship port areas. Finally, we compared the performance of our method with those of recently reported fire-detection approaches employing widely used performance matrices to test the fire classification results achieved.

Maritime Data Collection Framework for Container Port Benchmarking

Container ports play a crucial role in global maritime trade by serving as key transportation hubs for the movement of goods. Their performance differs due to unique local characteristics, creating a need for benchmarking to improve. Trends in trade patterns, supply chains and maritime transport operations can substantially impact on the cargo handling requirement at ports and therefore port performance. The aim of this research is two-fold; first, to examine the current situation, main trends and disruptions affecting maritime container transport and ports, and second, to investigate the key criteria for assessing and benchmarking port performance. Regarding the first objective, for the investigation of the global trends in the liner shipping and container port sector, desk research is performed. Regarding the second, a framework is developed by identifying five areas of interest based on the strategic value of data and selecting the best available data. This allows creating a two-layer data framework that enables the collection of data at the country and port level. The developed framework is applied to gather relevant data points for various container ports, and allows drawing conclusions on the performance of the framework. The results of this research contribute to the understanding of container port performance and may serve as a valuable resource for port operators and researchers in the field. By examining the specific factors that influence port performance, this study provides a foundation for enhancing the comparability of container port operational performance.

Study of Influencing Factors on CFD Method and Separated Flow by the Virtual Static Captive Test

In this paper, Computational Fluid Dynamic (CFD) methods are adopted to predict the manoeuvring behaviour of the KCS benchmark model. At first instance, the model is calibrated on the basis of three factors namely (a) turbulence model, (b) grid size and (c) time step. Secondly, a Static Oblique Towing Test (OTT) that utilises the SST K-ω turbulence model with a grid number of 1.64 million and a time step of 0.01 s is conducted. The maximum difference of the lateral force between the port and starboard ship sides is observed at x/Lpp = 0.055. Finally, application of Reynold Averaged Navier Stokes (RANS) and Dettached Eddy Simulation (DES) CFD models indicates that the latter may be preferable in terms of capturing the oblique flow field.

Research on the Investment Value of National Economy Stocks based on Factor Analysis

Scientific and technological innovation is a hot topic today, and companies on the STAR Board also have good performance and development prospects in the market. Many investors regard STAR Board stocks as key investment modules. This paper analyzes the factors influencing the investment value of the stocks of STAR Board enterprises listed from June to December 2021 in the first half of 2022. In this paper, nine indicators are selected to describe the market condition of each enterprise and the factors are narrowed down to four principal factors by factor analysis. Combined with the actual data analysis of the performance of each STAR Board enterprises in these four principal factors, score it to analyze the advantages and weaknesses of the corresponding enterprises of each stock, so as to provide investment reference for investors with investment needs. The dimension reduction and transformation function in SPSS are mainly utilized in entire research.

Subsidy policy optimization of multimodal transport on emission reduction considering carrier pricing game and shipping resilience: A case study of Shanghai port

To save energy and reduce carbon emissions, subsidy policies have been implemented to improve the competitiveness of railway and waterway transport in the multimodal transport system of port shipping. However, there is a lack of systematic research on the assessment of subsidy distribution with the consideration of shipping resilience and emission reduction. Disordered competition hinders the development of the multimodal transport market and also deters the achievement of carbon neutrality. To address these issues, this study proposes an optimized subsidy strategy with a bi-level subsidy decision-making model considering the pricing game between various carriers. Specifically, the amount of government subsidies is substituted into the upper model, and the carrier's pricing decision is obtained by solving the Nash equilibrium of the tripartite pricing game. An activity-based approach is used to calculate the emission reduction corresponding to the subsidy amount. The variance of the transport time is used to measure the resilience of transportation. Finally, the optimal subsidy strategy is obtained through quantitative comparison. This study employs the multimodal transport of containers based on Shanghai Yangshan Port as a case study. The sensitivity analysis discloses that the time value of goods and freight market conditions should raise more attention in the process of subsidy policy formulation. Moreover, the waterway carrier should try to improve resilience and reduce the risk of delay. This study innovatively considers the pricing game between highway, railway, and waterway carriers and provides a rational basis for the formulation of subsidy policies in multimodal transport through quantitative analysis.

Flow Structures and Aerodynamic Behavior of a Small-Scale Joined-Wing Aerial Vehicle under Subsonic Conditions

Flow behavior and aerodynamic performance of a small-scale joined-wing unmanned aerial vehicle (UAV) was studied experimentally and numerically under various pitch and yaw angle combinations in subsonic flow conditions. Selected numerical results are compared against experimental results obtained using surface oil flow visualizations and force measurements, with additional simulations expanding the range of combined pitch and yaw configurations. Under zero-yaw conditions, increasing the pitch angle leads to the formation of symmetric ogive vortex roll-ups close to the fuselage and their significant interactions with the fore-wing. Additionally, contributions to lift and drag coefficients under zero-yaw conditions by the key UAV components have been documented in detail. In contrast, when the UAV is subjected to combined pitch and yaw, no clear evidence of such ogive vortex roll-ups can be observed. Instead, asymmetric flow separations occur over the fuselage’s port side and resemble bluff-body flow behavior. Additionally, these flow separations become more complex, and they interact more with the fuselage and fore- and aft-wings when the yaw angle increases. Lift and drag variations due to different pitch and yaw angle combinations are also documented. Finally, rolling and yawing moment results suggest that the present UAV possesses adequate flight stability unless the pitch and yaw angles are high.

How liner shipping heals schedule disruption: A data-driven framework to uncover the strategic behavior of port-skipping

Service disruption hurts the reliability of liner shipping schedules and the lack of high-quality data impedes research on schedule recovery in liner shipping. With the support of the Automatic Identification System (AIS), a satellite-based tracking system acquiring real-time records of vessels’ navigation trajectories worldwide, this study develops a novel data-driven framework to uncover the vessel schedule disruption recovery behavior (i.e., port-skipping). The framework consists of a series of independent yet closely related algorithms, which are in turn tasked with screening port calls, estimating closed-loop routes, measuring similarity between different routes, and identifying port-skipping behavior, respectively. Compared with partial proprietary data from the shipping industry and port authorities, the identification results can provide transparent datasets with wide applications and much easier access publicly. The developed data-driven framework is implemented with vessel trajectory information of around 2,000 container vessels worldwide from January 2016 to December 2020, and the results prove its validity and practical value for liner shipping scheduling management.

A Study of Regional Variability in Port Transport Efficiency in China - Based on A Three-Stage DEA-Malmquist Index Model

To study the impact of COVID-19 on China's port logistics efficiency, this paper uses a three-stage DEA and Malmquist model to measure the efficiency of China's port logistics industry in 38 coastal and inland river cities under environmental constraints from 2016-2021, and analyses its spatial and temporal characteristics and evolutionary dynamics. The study shows that the comprehensive technical efficiency of port logistics in coastal cities is higher than that of inland river ports. The progress in port logistics efficiency stems from the increase in scale efficiency, and the dependence of port logistics on scale expansion is high. In addition, COVID-19 has had some hit on port operational efficiency, where the reduction in total factor productivity is mainly caused by a lower technological progress index. Compared to coastal ports, inland ports are more affected by COVID-19. Meanwhile, the impact of COVID-19 on the productivity of Chinese ports is characterised by a clear short-term pattern. The paper then makes recommendations for further improving the efficiency of port logistics and the ability to cope with major contingencies in China and globally.

Aktivitas Port Clearance Pengurusan Dokumen PT. Pelayaran Sumatra Timur Indonesia

A procurement for the smooth arrival and departure of ships (port clearance) must be in accordance with existing procedures so that ships can leave the port and arrive safely at the destination port. The purpose of this study is to determine activities related to the clearance of document management by PT. Pelayaran Sumatra Timur Indonesia. This research is a type of qualitative descriptive research. In the research method, there are two types of data in this study, namely primary data and secondary data. There are 3 methods of obtaining the data needed during the research time including observation methods, interview methods and documentation methods. The results showed that the clearance activity of PT. East Sumatra Indonesia shipping there are several procedures for handling documents on ships, namely, checking shipping orders, checking cargo manifests or loading lists and checking cargo shipping lists or boot notes and other important ship requirements. The document clearance procedure is divided into two, namely clearance in and clearance out. The clearance procedure is the process of obtaining permits for ships that will arrive at the port to related agencies. Clearance out the procedure on PT. East Sumatra Indonesia shipping includes after the ship has finished carrying out activities at the port and will again sail out of the port area, the agent re-requests the ship's departure or clearance.

Research on Multi-Port Ship Traffic Prediction Method Based on Spatiotemporal Graph Neural Networks

The intelligent maritime transportation system has emerged as a pivotal component in port management, owing to the rapid advancements in artificial intelligence and big data technology. Its essence lies in the application of digital modeling techniques, which leverage extensive ship data to facilitate efficient operations. In this regard, effective modeling and accurate prediction of the fluctuation patterns of ship traffic in multiple port regions will provide data support for trade analysis, port construction planning, and traffic safety management. In order to better express the potential interdependencies between ports, inspired by graph neural networks, this paper proposes a data-driven approach to construct a multi-port network and designs a spatiotemporal graph neural network model. The model incorporates graph attention networks and a dilated causal convolutional architecture to capture the temporal and spatial dimensions of traffic variation patterns. It also employs a gated-mechanism-based spatiotemporal bi-dimensional feature fusion strategy to handle the potential unequal relationships between the two dimensions of features. Compared to existing methods for port traffic prediction, this model fully considers the network characteristics of the overall port and fills the research gap in multi-port scenarios. In the experiments, real port ship traffic datasets were constructed using data from the Automatic Identification System (AIS) and port geographical information data for model validation. The results demonstrate that the model exhibits outstanding robustness and performs well in predicting traffic in multiple sub-regional port clusters.