Routing Information Research Articles

A high-effective swarm intelligence-based multi-robot cooperation method for target searching in unknown hazardous environments

To solve target searching problems for multi-robot cooperation with inaccurate target distance perception in unknown hazardous environments, a hybrid adaptive robotic particle swarm optimizer (RPSO) and grey wolf optimizer (GWO) based algorithm with continuous virtual target guidance is proposed for high effective path planning in the searching. In the initial searching stages, both the wolf behavior-generated position and the gbest position and the pbesti positions from RPSO are employed to guide the motions of robots. With the information provided by these initial robot movement paths, a geometric model is established to generate potential targets. The K-means cluster algorithm is introduced to estimate a virtual target position online from potential targets, with new robot-presenting route information to update the history path information. Then the virtual position is employed as one of the direction components to help the robots approach the actual target. In addition, to avoid mobile robots falling into local convergence, a heuristic moving direction determination scheme is utilized to make robots circumvent obstacles in swarm motions, as well as a mutual repulsion algorithm to keep them in a scattering state. Simulation experiments on different types of unknown environments with varied robot numbers and adaptability testing for a dynamic target are carried out to verify the feasibility of the proposed target searching method with comparisons to the other three famous target searching algorithms. It is verified from the results that the presented method can not only contribute a 100% success rate in all runs of searching for a stochastic dynamic target under a limited maximal velocity, but also produce both the shortest path length and minimum iterations in terms of statistical metrics over the comparative methods.

Decision-making optimization for post-disaster restoration of multimodal transport networks in terms of resilience

IntroductionPublic health emergencies can have a ripple effect on the resilience of multimodal transport network, which will lead to problems such as route disruptions or blockages, route selection change and information transmission delay spreading to the whole network, further hindering the transportation planning and operational efficiency of the network. MethodsThis study constructs a multimodal transport route optimization model under uncertainty with the objective of the sum of transportation cost, transshipment cost, penalty cost and carbon emission cost. To enhance the computational efficiency of the model, a novel invasive weed optimization with memory and encoding value clustering capabilities is proposed. In addition, by fusing the Q-learning algorithm in reinforcement learning with the novel invasive weed algorithm, the action-value function table obtained from the training facilitates the selection of optimal routes. Based on empirical data, explore the sensitivity analysis of node disruptions, time windows, and fuzzy demand on route decision-making under public health emergencies. ResultsThe transport network is affected by public health emergencies, which makes the optimal route deviate from the expected goal, resulting in an increase in the total cost. The proportion of total cost is determined by the position of nodes in the network, with critical nodes suffering more losses than ordinary nodes. Reasonable setting of time windows and fuzzy demand intervals is an effective way to improve the resilience and transportation efficiency of multimodal transport network. ConclusionsThis study provides more applicable decision-making references for enterprises to prevent the risk of supply chain disruptions caused by public health emergencies.

User Preference Maps: Quantifying the Built Environment

The built environment in which we live holds the potential to provide life experiences that allow pedestrians to observe, feel, learn, and grow through their surroundings in everyday urban spaces. If a city offers opportunities for careful observation and exploration according to users’ preferences, it will become more appealing to many people. This study selected Midtown, New York, as the research site and collected a total of seven datasets based on 30 intersections in the area. The data, categorized into three main areas—activity, comfort, and natural elements—were evaluated, visualized, and restructured using a path exploration algorithm to produce a final user-based map. For this, 3D modeling software Rhino version 7, visual programming tool Grasshopper, and Grasshopper verion 2023 plugin programs were used. The final result included 3D route information, quantitative measurement data, and multidimensional visual materials. This approach presents an alternative to traditional route navigation based on uniform criteria and, through data-driven design, is believed to ultimately enhance walkability, activate urban spaces, and contribute to the development of sustainable cities. The scope of related research can further expand as the targets, duration, and methods of data collection continue to evolve and as case studies in various cities increase.

Gambling Disorder among Croatian University Students

Abstract Background Gambling has become a routine form of entertainment for many young people studying in universities. This study aimed to investigate the prevalence and characteristics of gambling disorder among Croatian university students. Methods A cross-sectional study using an anonymous self-administered questionnaire with sociodemographic and socioeconomic questions along with the Croatian version of the South Oaks Gambling Screen questionnaire (SOGS-CRO) was conducted in a representative sample of Croatian university students from Osijek, Eastern Croatia during January, and February 2020. Results A total of 844 students participated in the study, 45.6% males and 54.4% females, with a median age of 19.00 years (interquartile range 19.00-23.00 years). Gambling activities were performed by 66.2% of the students, most commonly lotteries, sports bets, and bingo. The final scores of the SOGS-CRO suggested that 21.1% of students may have a gambling disorder, with 16.4% being “probable pathological gamblers” and 4.7% being “problem gamblers”. Playing cards for money, animal bets, stocks bets, slot machines bets, pull-tabs, and other gambling activities such as online or videogames or mobile games present a higher risk of gambling disorder (p < 0.001, p < 0.001, p < 0.001, p < 0.001, p < 0.001, p < 0.001, respectively). Being male, repeating the year of study, being a student in interdisciplinary or technical field of study, and having a mother with gambling disorder (p < 0.001, p = 0.029, p < 0.001, p < 0.001, respectively) were the revealed contributing factors to the development of gambling disorder in study participants. Conclusions The study points to the fact that gambling disorder is quite a prevalent public health issue in the Croatian university student population. Specific public health preventive programs need to be designed and implemented in the study population to successfully prevent the development of investigated behavioral addiction. Key messages • Public health interventions directed toward the prevention of gambling must involve younger population subgroups, such as university students. • When designing the preventive programs for the successful prevention of gambling within the university student population selected sociodemographic variables should be considered.

Institutional Imprints and Corporate Misconduct: Unravelling the Interplay of Economic History and Firm Choices on Earnings Manipulation in an Emerging Economy

This study investigates the impact of firms’ legacy institutional imprints on its engagement in corporate misconduct. We discover that a closed economic regime’s protectionist policies inscribe imprints in the form of opaque organizational routines and cause incumbent firms to develop competitive limitations. Utilizing the theoretical principles of the organizational imprinting theory, this research attests to the endurance of corruptive routines and argues that the degree of closed economy imprints increases firms’ engagement in income-increasing earnings management in the post-liberalization period. Furthermore, we find that the impact of imprints is weakened by firms’ choices on international exposure and internal innovation. By utilizing the historical data on Indian economic policies from 1956 to 1991 and analyzing a sample of 18,432 firm-year observations for 2,396 listed Indian manufacturing firms from 1997 to 2007, we find support for our hypotheses.

Border Gateway Protocol Route Leak Detection Technique Based on Graph Features and Machine Learning

In the Internet, ASs are interconnected using BGP. However, due to a lack of security considerations in the design of BGP, a series of security issues arise during the propagation of routing information, such as prefix hijacking, route leakage, and AS path tampering. Therefore, this paper conducts research on the detection of route leakage. By analyzing BGP routing information, we abstract the routing propagation relationship between ASs into a network topology graph, and extract graph features from the graph abstracted from routing data at certain time intervals. Based on the structural robustness features and centrality measurement features of the graph, we determine whether a route leakage has occurred during the current time period. To this end, we use machine learning methods and propose a weighted voting model. This model trains multiple single models and assigns weights to them, and through the weighted analysis of the results of multiple models, it can determine whether a route leakage has occurred. In addition, to determine the corresponding weights, we use genetic algorithms for identifying route leaks. The experimental results show that the method used in this paper has a high accuracy rate, and compared with a single model, it performs better on multiple datasets.

Exploiting Queue Information for Scalable Delay-Constrained Routing in Deterministic Networks

Exploiting Queue Information for Scalable Delay-Constrained Routing in Deterministic Networks

Optimizing Capsule Network Performance with Enhanced Squash Function for Classification Large-Scale Bone Marrow Cells

Capsule networks (CapsNet) have emerged as a promising architectural framework for various machine-learning tasks and offer advantages in capturing hierarchical relationships and spatial hierarchies within data. One of the most crucial components of CapsNet is the squash function, which plays a pivotal role in transforming capsule activations. Despite the success achieved by standard squash functions, some limitations remain. The difficulty learning complex patterns with small vectors and vanishing gradients are major limitations. Standard squash functions may struggle to handle large datasets. We improve our methodology to enhance squash functions to address these challenges and build on our previous research, which recommended enhancing squash functions for future improvements. Thus, high-dimensional, and complex data scenarios improve CapsNet’s performance. Enhancing CapsNet for complex tasks like bone marrow (BM) cell classification requires optimizing its fundamental operations. Additionally, the squash function affects feature representation and routing dynamics. Additionally, this enhancement improves feature representation, preserves spatial relationships, and reduces routing information loss. The proposed method increased BM data classification accuracy from 96.99% to 98.52%. This shows that our method improves CapsNet performance, especially in complex and large-scale tasks like BM cells. Comparing the improved CapsNet model to the standard CapsNet across datasets supports the results. The enhanced squash CapsNet outperforms the standard model on MNIST, CIFAR-10, and Fashion MNIST with an accuracy of 99.83%, 73%, and 94.66%, respectively. These findings show that the enhanced squash function improves CapsNet performance across diverse datasets, confirms its potential for real-world machine learning applications, and highlight the necessity for additional research.

From operational to strategic modelling: A continuous multi-scale approach for last-mile analysis

Supply chain planning requires decision-making at all levels, especially in the new normal of intervened supply networks. The integration of strategic network design and operational routing decisions has been widely studied in the literature as the location-routing problem (LRP). However, the LRP does not consider the differences in the planning horizon of each sub-problem, nor have geospatial elements been included in the distribution of demand. This paper aims to redesign the conceptual modelling of the last mile to inform strategic network design problems. A continuous multi-scale approach (CMA) is proposed by taking elements from the districting problem (DP) and continuous approximation (CA). This approach includes stochastic demand in the analysis and the effects of time windows and failed deliveries. The validation of CMA in a case of rural parcel deliveries in Belgium shows an estimate of the distance travelled similar to traditional routing algorithms in scenarios with high demand density. Likewise, the effects of time windows on the spatial configuration of multi-scale districting are explored. This approach provides insights for decision-making in strategic and tactical planning, such as sonification, differentiated services to satisfy consumer preferences, and fleet management. The limitations of the CMA lie in its implementation in purely operational scenarios since it does not offer detailed routing information. Subsequent research aims to exploit the potential of strategic last-mile modelling with effective integration into network design problems.

Deformable Capsules for Object Detection

Capsule networks promise significant benefits over convolutional neural networks (CNN) by storing stronger internal representations and routing information based on the agreement between intermediate representations’ projections. Despite this, their success has been limited to small‐scale classification datasets due to their computationally expensive nature. Though memory‐efficient, convolutional capsules impose geometric constraints that fundamentally limit the ability of capsules to model the pose/deformation of objects. Further, they do not address the bigger memory concern of class capsules scaling up to bigger tasks such as detection or large‐scale classification. Herein, a new family of capsule networks, deformable capsules (DeformCaps), is introduced to address object detection problem in computer vision. Two new algorithms associated with our DeformCaps, a novel capsule structure (SplitCaps), and a novel dynamic routing algorithm (SE‐Routing), which balance computational efficiency with the need for modeling a large number of objects and classes, are proposed. This has never been achieved with capsule networks before. The proposed methods efficiently scale up to create the first‐ever capsule network for object detection in the literature. The proposed architecture is a one‐stage detection framework and it obtains results on microsoft common objects in context which are on par with state‐of‐the‐art one‐stage CNN‐based methods, while producing fewer false‐positive detection, generalizing to unusual poses/viewpoints of objects.

Energy-Efficient Clustering in Wireless Sensor Networks Using Grey Wolf Optimization and Enhanced CSMA/CA.

Survivability is a critical concern in WSNs, heavily influenced by energy efficiency. Addressing severe energy constraints in WSNs requires solutions that meet application goals while prolonging network life. This paper presents an Energy Optimization Approach (EOAMRCL) for WSNs, integrating the Grey Wolf Optimization (GWO) for enhanced performance. EOAMRCL aims to enhance energy efficiency by selecting the optimal duty-cycle schedule, transmission power, and routing paths. The proposed approach employs a centralized strategy using a hierarchical network architecture. During the cluster formation phase, an objective function, augmented with GWO, determines the ideal cluster heads (CHs). The routing protocol then selects routes with minimal energy consumption for data transmission to CHs, using transmission power as a metric. In the transmission phase, the MAC layer forms a duty-cycle schedule based on cross-layer routing information, enabling nodes to switch between active and sleep modes according to their network allocation vectors (NAVs). This process is further optimized by an enhanced CSMA/CA mechanism, which incorporates sleep/activate modes and pairing nodes to alternate between active and sleep states. This integration reduces collisions, improves channel assessment accuracy, and lowers energy consumption, thereby enhancing overall network performance. EOAMRCL was evaluated in a MATLAB environment, demonstrating superior performance compared with EEUC, DWEHC, and CGA-GWO protocols, particularly in terms of network lifetime and energy consumption. This highlights the effectiveness of integrating GWO and the updated CSMA/CA mechanism in achieving optimal energy efficiency and network performance.

Managing Trouble Along a Continuum of Accountability: On Police Practices for Dealing with Less-Than-Competent Subjects

Abstract This article uses conversation analysis to examine recordings of police encounters, focusing on how officers respond to routine forms of trouble emerging from civilians’ failure to comprehend and cooperate with their initiating actions. The analysis demonstrates how officers improvise methods to deal with their subjects’ troublesome responses to their queries and directives, treating civilians along a continuum from being purposefully uncooperative to lacking the interactional competence to comply with their projects. In the latter case, officers treat noncompliance as related to one’s status as “mentally disordered,” adopting practices to progressively carve out islands of competence within which civilians are competent to cooperate or accountable for not doing so. Thus, officers’ orientation to a continuum of accountability shapes the encounter’s interactional trajectory toward more punitive or instructive outcomes.

Aerosol inhalation of total ginsenosides repairs acute lung injury and inhibits pulmonary fibrosis through SMAD2 signaling-mediated mechanism

BackgroundPulmonary fibrosis (PF) is a progressive lung disease caused by previous acute lung injury (ALI), but there is currently no satisfactory therapy available. Aerosol inhalation of medicine is an effective way for treating PF. Total ginsenosides (TG) shows potential for the treatment of ALI and PF, but the effects of inhaled TG remain unclear. PurposeTo determine the therapeutic effects of TG in ALI and PF, to assess the superiority of the inhaled form of TG over the routine form, and to clarify the mechanism of action of inhaled TG. MethodsUltrahigh-performance liquid chromatography coupled with Q-Exactive Orbitrap mass spectrometry (UPLC-QE-MS) was applied to determine the chemoprofile of TG. A mouse model of ALI and PF was established to evaluate the effects of inhaled TG by using bronchoalveolar lavage fluid (BALF) analysis, histopathological observation, hydroxyproline assay, and immunohistochemical analysis. Primary mouse lung fibroblasts (MLF) and human lung fibroblast cell line (HFL1) were applied to determine the in vitro effects and mechanism of TG by using cell viability assay, quantitative real time PCR (qPCR) assay, and western blot (WB) analysis. ResultsThe UPLC-QE-MS results revealed the main types of ginsenosides in TG, including Re (14.15 ± 0.42%), Rd (8.42 ± 0.49%), Rg1 (6.22 ± 0.42%), Rb3 (3.28 ± 0.01%), Rb2 (3.09 ± 0.00%), Rc (2.33 ± 0.01%), Rg2 (2.09 ± 0.04%), Rb1 (1.43 ± 0.24%), and Rf (0.13 ± 0.06%). Inhaled TG, at dosages of 10, 20, and 30 mg/kg significantly alleviated both ALI and PF in mice. Analyses of BALF and HE staining revealed that TG modulated the levels of IFN-γ, IL-1β, and TGF-β1, reduced inflammatory cell infiltration, and restored the alveolar architecture of the lung tissues. Furthermore, HE and Masson's trichrome staining demonstrated that TG markedly decreased fibroblastic foci and collagen fiber deposition, evidenced by the reduction of blue-stained collagen fibers. Hydroxyproline assay and immunohistochemical analyses indicated that TG significantly decreased hydroxyproline level and down-regulated the expression of Col1a1, Col3a1, and α-sma. The inhaled administration of TG demonstrated enhanced efficacy over the oral route when comparable doses were used. Additionally, inhaled TG showed superior safety and therapeutic profiles compared to pirfenidone, as evidenced by a CCK8 assay, which confirmed that TG concentrations ranging from 20 to 120 μg/ml were non-cytotoxic. qPCR and WB analyses revealed that TG, at concentrations of 25, 50, and 100 μg/ml, significantly suppressed the phosphorylation of smad2 induced by TGF-β1 and down-regulated the expression of fibrotic genes and proteins, including α-sma, Col1a1, Col3a1, and FN1, suggesting an anti-fibrotic mechanism mediated by the smad2 signaling pathway. In vitro, TG's safety and efficacy were also found to be superior to those of pirfenidone. ConclusionsThis study demonstrates, for the first time, the therapeutic efficacy of inhaled TG in treating ALI and PF. Inhaled TG effectively inhibits inflammation and reduces collagen deposition, with a particular emphasis on its role in modulating the Smad2 signaling pathway, which is implicated in the anti-fibrotic mechanism of TG. The study also highlights the superiority of inhaled TG over the oral route and its favorable safety profile in comparison to pirfenidone, positioning it as an ideal alternative for ALI and PF therapy.

A energy efficiency optimization routing processing method for Linear Wireless Sensor Networks

Linear wireless Sensor networks (LWSNs) are extensively utilized to monitor linear infrastructure such as railways, bridges, mines, and borders. In the large-scale deployment of sensor networks, extending network life and improving network energy balance remains a significant challenge. The existing routing protocols adopt the path planning method based on the routing information database, which can effectively transmit data packets. However, the methods consume additional energy when establishing and maintaining routing tables, significantly reducing the nodes’ lifespan and leading to unstable data transmission quality. This paper proposes a dynamic random multipath routing method (DRMRM) for LWSNs. The technique combines the node depth and residual energy models to select the optimal next-hop relay node without relying on the transmission routing table. At the same time, we designed a data loss retransmission mechanism and a data loop retreat mechanism to prevent data packets from reaching a dead end. The experimental results demonstrate that our routing method is superior to existing energy consumption balance and network lifespan protocols.

Cultural Institution in the 21st Century: The Need, Tradition, and Construct (A Study on the Russian Population in the Republic of Tatarstan)

This article examines the conceptualization of the institutional forms of ethno-social daily routines among the modern Russian population of the Republic of Tatarstan. The basic cultural institutions, such as ethnicity, religious identity, family, and ethno-cultural practices, are described. Their role across different groups of the Russian population in Tatarstan is revealed. The analysis is based on a multi-paradigm approach and well-known socio-humanitarian theories (functionalism, phenomenology, and social constructivism). The study «Social well-being and ethno-cultural needs of the Russian population in the Republic of Tatarstan» carried out in autumn 2022 is used as an analytical source of data. The obtained results show that the demand for traditional cultural institutions, which can be categorized as «need», «tradition», and «construct», varies depending on their roles within the study groups. A growing influence of urban spaces in shaping the ethnic «tradition», particularly among young people, is shown.

Energy Efficient strategy for Wireless Sensor Networks lifetime endurance using Genetic Algorithm

This study propose an optimized routing scheme for multiple hops Wireless Sensor Networks to enhance the lifetime of the network. Since the wireless sensor networks work as application-centric having the capabilities of data gathering and transmission to the base station. Therefore, a significant amount of sensor networks` energy spends during such operation of data collection and transmission if the route is not optimized. So, a meta-heuristic genetic algorithm plays a vital role in finding the optimized route. We will modify the genetic algorithm for better optimization results so that the best route can be selected among all possible routes and will save the wireless sensor networks` energy. All information is routed towards the sink via intermediate neighbor nodes, which only relay the data without performing any computation or operation. The genetic algorithm determines the route information to all nodes and sinks. Then, the data takes the most optimal route for data transmission. Thus, this study proved a considerably saved extra waste of energy.

Transforming Network Management: Intent-Based Flexible Control Empowered by Efficient Flow-Centric Visibility

The Internet architecture has recently shifted towards a framework characterized by multiple interconnected cloud sites, all linked via an L3 IP network. With this shift, managing networking controls among multiple cloud sites is becoming a significant operational challenge. In particular, ensuring effective networking control necessitates a deeper understanding of flow-level dynamics for comprehensively monitoring interconnection statuses across multiple sites. In this paper, we first propose an IO Visor-enabled tracing solution for Linux-based boxes to efficiently enable the comprehensive collection of network packet flows across interconnected sites. Next, we apply IP prefix-based flow-level analysis at a centralized location to support the intent-based networking control application. This flow-level analysis involves generating policy-based specific action (i.e., redirect) via SDN controllers for specific source IP prefixes, which are causing unknown or potentially vulnerable flows. Furthermore, we employ an open-source ONOS SDN controller to tackle the challenge of managing the hybrid SDN-IP interconnections. By leveraging intent-based networking control, we effectively apply ONOS intents based on IP routing information and generated a set of forwarding action. We evaluate our proposed solution in an experimental SDN-cloud testbed, demonstrating its effectiveness in real-world scenarios. Overall, through the seamless integration of these monitoring and control approaches, we manage to enhance the adaptability and security of the interconnected cloud sites of the testbed.

Machine learning attack detection based-on stochastic classifier methods for enhancing of routing security in wireless sensor networks

Wireless Sensor Networks (WSNs) are vulnerable to attacks during data transmission, and many techniques have been proposed to detect and secure routing data. In this paper, we introduce a novel stochastic predictive machine learning approach designed to discern untrustworthy events and unreliable routing attributes, aiming to establish an artificial intelligence-based attack detection system for WSNs. Our methodology leverages real-time analysis of the features of simulated WSN routing data. By integrating Hidden Markov Models (HMM) with Gaussian Mixture Models (GMM), we develop a robust classification framework. This framework effectively identifies outliers, pinpoints malicious network behaviors from their origins, and categorizes them as either trusted or untrusted network activities. In addition, dimensionality reduction techniques are used to improve interpretability, reduce computation and processing time, extract uncorrelated features from network data, and optimize performances. The main advantage of our approach is to establish an efficient stochastic machine learning method capable of analyzing and filtering WSN traffic to prevent suspicious and unsafe data, reduce the large dissimilarity in the collected routing features, and rapidly detect attacks before they occur. In this work, we exploit a well-tuned data set that provides a lot of routing information without losing any data. The experimental results show that the proposed stochastic attack detection system can effectively identify and categorize anomalies in wireless sensor networks with high accuracy. The classification rates of the system were found to be around 83.65%, 84.94% and 94.55%, which is significantly better than the existing classification approaches. Furthermore, the proposed system showed a positive prediction value of 11.84% higher than the existing approaches.

Secure Routing and Reliable Packets Transmission In MANET Using Fast Recursive Transfer Algorithm

Mobile Ad-hoc Network (MANET) autonomous operation can be multi-hop it is the infrastructure-less wireless network. Security is one of the biggest challenges in Mobile Adhoc Network. The MANETs security there are considerations must be so that the routing protocol in order to protect the secure data transmission. In the routing and security that is an important aspect for in a MANETs, existing method routing protocol, however, is not enough to security requirements. The proposed method using Fast Recursive Transmission Algorithm (FRTA) used designed to maximize the data security, routing optimization, minimizing the impact of malicious attack using Collision Detection Avoid Algorithm (CDAA) activity over the MANET and select the best path. Table-driven routing protocols, also known as proactive routing protocols, mandate that every node on the network keep up-to-date routing data. To maintain consistent routing information for network nodes, these protocols propagate frequent updates to the routing table throughout the network, necessitating changes to the network topology. There's a lot of overhead from these upgrades. The suggested FRTA algorithm lowers node data loss rates and increases network energy efficiency. Table-driven routing protocols, also known as proactive routing protocols, mandate that every node on the network keep up-to-date routing data. To maintain consistent routing information for network nodes, these protocols propagate frequent updates to the routing table throughout the network, necessitating changes to the network topology. There's a lot of overhead from these upgrades. The suggested FRTA algorithm lowers node data loss rates and increases network energy efficiency. The proposed method shows high performance than other existing evaluations of the most advanced security and routing energy, end-to-end delay, packet transfer rate, packet loss

Certificate of Origin Management in Supporting the Export Process: A Qualitative Descriptive Analysis on Marine Cargo Expeditions

The complexity of managing export documentation and the lack of knowledge of regulations in the importing country cause non-fulfilment of requirements and cancellation of exports. This study aims to determine the stages and the mechanism for managing certificates of origin in the export process by freight forwarding or marine cargo expedition representing the exporter. The research used qualitative descriptive methods and approaches. Data was collected by observation and documentation at two marine cargo expedition companies in Semarang, Central Java, Indonesia. Data is analyzed and presented using a flowchart diagram. The results show that these companies manage COO documents through the eSKA website, with the completeness of filling in the basic information of exporters, consignees, route and transport, and supporting documents, including export declaration, packing list, invoice, goods, and cost structure. Overall, export documents are indispensable for international trade as they ensure goods' legal compliance and smooth flow across borders. The role of the freight forwarding company or marine cargo expedition in this process is to advise the exporter on the required paperwork, ensure compliance with relevant regulations, and communicate with the appropriate authorities to obtain the certificate. The positive implications of this research are that the administration of export documentation, particularly Certificates of Origin (COO) via the eSKA platform by marine cargo expeditions, enhances time efficiency for exporters and proves efficacious in ensuring the legal compliance of COO document handling. Moreover, COOs can yield favourable effects through preferential tariffs, thereby augmenting the competitiveness of exported goods.