Basis For Decisions Research Articles

Novel travel time aware metapopulation models and multi-layer waning immunity for late-phase epidemic and endemic scenarios.

In the realm of infectious disease control, accurate modeling of the transmission dynamics is pivotal. As human mobility and commuting patterns are key components of communicable disease spread, we introduce a novel travel time aware metapopulation model. Our model aims to enhance estimations of disease transmission. By providing more reliable assessments on the efficacy of interventions, curtailing personal rights or human mobility behavior through interventions can be minimized. The proposed model is an advancement over traditional compartmental models, integrating explicit transmission on travel and commute, a factor available in agent-based models but often neglected with metapopulation models. Our approach employs a multi-edge graph ODE-based (Graph-ODE) model, which represents the intricate interplay between mobility and disease spread. This granular modeling is particularly important when assessing the dynamics in densely connected urban areas or when heterogeneous structures across entire countries have to be assessed. The given approach can be coupled with any kind of ODE-based model. In addition, we propose a novel multi-layer waning immunity model that integrates waning of different paces for protection against mild and severe courses of the disease. As this is of particular interest for late-phase epidemic or endemic scenarios, we consider the late-phase of SARS-CoV-2 in Germany. The results of this work show that accounting for resolved mobility significantly influences the pattern of outbreaks. The improved model provides a refined tool for predicting outbreak trajectories and evaluating intervention strategies in relation to mobility by allowing us to assess the transmission that result on traveling. The insights derived from this model can serve as a basis for decisions on the implementation or suspension of interventions, such as mandatory masks on public transportation. Eventually, our model contributes to maintaining mobility as a social good while reducing exuberant disease dynamics potentially driven by travel activities.

The Neural Basis of Moral Decision-Making. Bridging Neurobiology and Theology

This study sightsees the complex relationship between neurobiology and theology in the context of moral decision-making, by utilizing a multidisciplinary approach, the research integrates findings from cognitive neuroscience, neuroimaging studies, and theological analysis to examine the neural pathways that lie beneath ethical judgments. Our paper also investigates the role of cognitive heritage in shaping moral reasoning and how neuroplasticity influences the development and adaptation of ethical frameworks. Theological perspectives are considered to offer deeper insights into human moral behaviour and in addition, the paper addresses the ethical implications of neurobiological research, emphasizing the importance of bridging scientific and religious perspectives. Through a synthesis of empirical data and theological discourse, this work tries to underwrite to a more comprehensive understanding of moral decision-making, offering insights that are both scientifically grounded and spiritually informed.

MLDM: A Multi Learning Domain Model for Fault Identification of Centrifugal Fan

Abstract In centrifugal fan fault diagnosis, the influence of multiple noise sources causes the collected fault signals to contain interference of different modes. Therefore, the difficulty of capturing fault characteristics greatly increases in this situation. In this article, a Multi Learning Domain Model (MLDM) scheme is proposed based on signal processing technology and artificial intelligence methods. The Haar Wavelet Convolution Extraction (HaarCE) module in the scheme is used to synchronously map features at different frequency scales, while the Lightweight Multi-Scale Feature Enhancement (LMFE) module enhances feature extraction at different time scales. Meanwhile, Upsampling is employed to enhance the expression of fault features. In addition, applying Information Rectification Learning (IRL) to feature maps extracted at each level allows for spatial representation and selection of extracted fault features, providing a basis for final decision-making. This scheme performs multi-level and multi-scale analysis on fault signals, jointly extracting time, frequency, and spatial information to improve the robustness and generalization ability of the model. Conduct experimental verification using data from the same type of centrifugal fan and rotor. The experimental results show that the recognition accuracy of the proposed model is about 93% for fan data at SNR=-4dB, which has certain competitiveness compared to other excellent models.

Risk Assessment of Small-Diameter Shield Construction in a Deep Drainage Tunnel Based on an ISM–CRITIC–Cloud Model

The deep drainage tunnel project is an important measure to alleviate urban waterlogging. The construction of a deep drainage tunnel is a complicated process, involving many influencing factors, and there are correlations among these influencing factors, so the risk assessment is difficult. In this study, the ISM method, CRITIC method and cloud model are combined to build a risk assessment model for the small-diameter shield construction of a deep drainage tunnel. Firstly, the risk index system of small-diameter shield construction in a deep drainage tunnel is put forward. Secondly, the ISM method is used to divide the risk indicators and extract the key risk factors. Then, these key risk factors are weighted with the CRITIC method, and the cloud model is used to evaluate the construction risk of a small-diameter shield of a deep drainage tunnel. Finally, the feasibility and accuracy of the proposed method are verified by a practical case. It was found that the risk assessment method proposed in this study can not only effectively assess the level of security risk, but also identify the key risk factors and rank the importance of these factors. The results of this study can reduce the interference items and workload of risk assessment to a certain extent, and help provide managers with an accurate decision-making basis.

A Critical Analysis of the Integration of Evidence-Based Practice among Nurses and Nursing Assistants and Its Impact on Patient-Centered Care in Multidisciplinary Healthcare Teams

This article focuses on the implementation of EBP concerning nurses and nursing assistants and studies the effects of that implementation on the properly targeted and patient-specialized healthcare process with the help of MDTs. EBP is critical in today’s healthcare needs since it allows for the basis for decision-making and frames the latest practices influencing patients' health. Nevertheless, there are challenges that nursing assistants face when implementing EBP in clinics because they have no formal training in EBPs. From the literature review and synthesis of this paper, we demonstrate the elements of EBP that improve team communication, patient satisfaction, and decreased medical errors, but what has been left unanswered is the experiences of the nursing assistants in EBP. Future strategies include evidence-based practice specialist schooling and hiring to catalyze increased EBP across multiple discipline teams and promote highly effective patient care.

Quantitative assessment of the relative impacts of climate change and human activities on net primary productivity of vegetation in the Beijing-Tianjin-Hebei region

IntroductionAs an important part of terrestrial ecosystem, vegetation plays a vital role in the terrestrial carbon cycle. As one of China’s three major urban clusters, the Beijing-Tianjin-Hebei region has experienced significant changes in vegetation due to climate change and large-scale ecological restoration efforts. Climate change and human activities are two factors that have important effects on the carbon cycles of terrestrial ecosystems. Quantitative assessment of the relative effects of climate change and human activities on vegetation dynamics is of paramount importance for regional sustainable development and ecological security.MethodsIn this study, based on ground meteorological data and remote sensing data including NDVI, an improved Carnegie-Ames-Stanford Approach model was employed to estimate the net primary productivity (NPP) of vegetation in the region from 2000 to 2020. Utilizing methods such as trend analysis, Mann-Kendall significance test, and correlation analysis, we investigated the spatiotemporal patterns of vegetation NPP and its correlations with precipitation, temperature, and solar radiation. Furthermore, the partial derivative trend residuals method was adopted to separate and quantify the impacts of climate change and human activities on vegetation NPP in different years.ResultsThe results show that: (1) from 2000 to 2020, the overall trend of vegetation NPP in the Beijing-Tianjin-Hebei region exhibited an increasing trend, with a spatial distribution pattern showing a step-like distribution from low to high to low from northwest to southeast. The multi-year average NPP was 384.90 g Cm−2, with an annual average growth rate of 3.00 g Cm−2. The NPP values of different vegetation types from large to small were: forests (578.40 g C m−2), shrubland (386.57 g C m−2), grassland (380.17 g C m−2), and cropland (324.91 g C m−2). (2) The proportion of regions with a positive correlation coefficient between average temperature and vegetation NPP in the Beijing-Tianjin-Hebei region from 2000 to 2020 was 69.85%, while the proportions of regions with positive correlations between precipitation and solar radiation with vegetation NPP were 92.1% and 89.19%, respectively. This indicates that vegetation NPP in most regions is positively correlated with precipitation, solar radiation, and temperature, with precipitation exerting a greater influence on vegetation NPP than temperature and solar radiation. (3) Vegetation improvement in the Beijing-Tianjin-Hebei region is mainly driven by both climatic factors and human activities, while vegetation degradation is primarily caused by human factors. The relative contributions of climate change and human activities to changes in vegetation NPP in the study area are 45.11% and 54.89%, respectively. The areas where they positively contribute account for 48.64% and 73.95% of the total study area, respectively, with the proportions of areas with relative contribution rates >60% being 45.86% and 28.86%, respectively.DiscussionThe findings of this study can provide scientific basis for vegetation restoration, low-carbon development, and ecosystem management decision-making in the Beijing-Tianjin-Hebei region.

Analisis Hubungan Kemampuan Membaca Al-Qur’an dengan Pemahaman Ilmu Tajwid Mahasiswa PAI UIN Imam Bonjol Padang

This study aims to examine the relationship between the ability to read the Qur'an and the understanding of Tajweed among Islamic Religious Education (PAI) students at UIN Imam Bonjol Padang. A good ability to read the Qur'an is expected to show a deep understanding of the science of tajweed, which includes the rules and regulations of reading in accordance with the provisions of the laws of tajweed. This study uses a quantitative approach with a correlational method to analyze the relationship between the two variables with a sample of 20 people from 150 populations by sampling with random sampling. The data were obtained by distributing questionnaires about the ability to read the Qur'an and understanding the science of Tajweed. The correlation analysis technique is used to see if there is a significant relationship between reading ability and understanding of Tajweed. The results showed that there is a significant positive correlation between the ability to read the Qur'an of PAI students of UIN Imam Bonjol Padang and the understanding of Tajweed. Based on the results of test analysis conducted on SPSS 20, the sig Asymp. Sig. (2-tailed) of 0.921> 0.05. Thus, in accordance with the basis of decision in the Kolmogorov-Smirnov normality test, it can be concluded that the data are normally distributed. Thus, the assumption or requirement of normality in the regression model is met.

Tyrosine Hydroxylase-positive Nucleus Accumbens Neurons Influence Delay Discounting in a Mouse T-maze Task.

Delay discounting (DD) is a phenomenon where individuals devalue a reward associated with a temporal delay, with the rate of devaluation being representative of impulsive-like behavior. Here we first sought to develop and validate a mouse DD task to study brain circuits involved in DD decision-making within short developmental time windows, given widespread evidence of developmental regulation of impulse control and risk-taking. We optimized a T-maze DD task for mice that enables training and DD trials within two weeks. Mice learned to choose between a large and a small reward located at opposite arms of a T-maze. Once training criteria were met, mice underwent DD whereby the large reward choice was associated with a temporal delay. Task validation showed that adolescent C57BL/6J mice display increased preference for the small reward upon a temporal delay, confirming increased impulsivity compared to adults. We next used this DD task to explore the neural basis of decision-making. We used tyrosine hydroxylase transgenic mice (TH-Cre) to target TH-positive neurons in the nucleus accumbens (NAc) and ventral tegmental area (VTA) with Cre-dependent excitatory or inhibitory Designer Receptors Exclusively Activated by Designer Drugs (DREADDs). Inhibition of transduced neurons in the NAc decreased preference for the small but immediate reward during DD. Inhibition of TH+ neurons in the ventral tegmental area (VTA) did not affect impulsive choice in this DD task. These results uncover a novel role for NAc TH-positive neurons in DD behavior and expand the repertoire of behavioral tasks available for studying decision-making across the lifespan.Significance Statement Delay discounting (DD) tasks are used in rodents to study impulsive choice, whereby subjects display a preference for an immediate, smaller reward when access to a larger reward is contingent on a temporal delay. Research implicates the nucleus accumbens (NAc) brain region in impulsive behavior, with recent evidence of specialization among NAc neuronal subtypes in impulsive choice. Here we interrogated the neural requirements of impulsive choice in mice. We found that inhibition of a subset of NAc neurons expressing tyrosine hydroxylase decreases impulsive choice. We also saw increased impulsive choice in adolescent mice compared to adults, consistent with reported developmental changes in impulsivity. Together, our data identify cell-specific NAc regulation of impulsive choice with important implications for neurodevelopment.

A hundred years of Pacific halibut management in the context of global events and trends in fisheries management

The Convention for the Preservation of the Halibut Fisheries of the Northern Pacific Ocean, signed between Canada and the United States of America on 2 March 1923 and ratified on 21 October 1924, established the International Fisheries Commission, renamed in 1953 to the International Pacific Halibut Commission (IPHC). It was the first international agreement for joint management of a marine fishery resource and a major milestone for development of modern standards for marine conservation. The IPHC’s centennial year is an opportunity to celebrate a remarkable history of the Commission, but also reflect on challenges that shaped its mission. Born from alarms about overfishing during World War I, the Commission gradually gained authority to implement a wide range of conservation measures through established public confidence in its basis for decisions. This paper explores the evolution of management measures applied to Pacific halibut commercial fishing shaped not only by the changing stock conditions and growing demand for seafood, but also global events and trends in fisheries management. It examines the impact of rapid commercialization of fisheries driven by population growth and technological improvements, establishment of exclusive economic zones and altered access to fishing grounds, and adoption of Agenda 21, which highlighted the importance of balancing environmental, economic, and social aspects in fisheries management. It concludes by discussing the lessons learned over the past century and their implications for future fisheries management, emphasizing the importance of international cooperation, adaptive strategies, and science-based policies in sustaining transboundary fish stocks like the Pacific halibut.

Pendampingan Penyusunan Laporan Keuangan bagi UMKM

This community service aims to strengthen the empowerment of Micro, Small, and Medium Enterprises (MSMEs) in South Sulawesi through assistance in preparing financial statements. Through a series of workshops, trainings, and individual mentoring sessions, we provide an in-depth understanding of the significance of financial statements and equip MSME owners with practical skills to compile, read, and analyze their financial statements. The mentoring method involves organizing interactive workshops led by experienced financial practitioners and individual mentoring sessions tailored to the needs of each MSME. In addition, we provide online resources as a learning platform that MSMEs can access flexibly, including guides, financial statement templates, and relevant case studies. Outcomes of the service include improved MSMEs' understanding of financial reporting, significant financial empowerment, and increased access to external financial support. MSMEs that participated in the program were able to produce clear financial statements, providing a basis for smarter decision-making and increasing their chances of securing financial support from financial institutions. With a focus on the context of South Sulawesi, this service contributes to the strengthening of the MSME sector in the region, supports local economic growth, and positively impacts the financial sustainability of MSMEs. Through assistance in preparing financial statements, it is hoped that MSMEs in South Sulawesi can develop sustainably and contribute optimally to regional economic development

STRATEGY OF IMPROVEMENT OF THE LOGISTICAL PROCESSES IN AN ENTERPRISE

The e-commerce industry is growing in strength year by year due to the growth in the number of consumers using this solution. The market of e-commerce industry allows reaching to a broader group of customers and reduction of costs. In order to gain a competitive advantage, any enterprise must work out an appropriate plan, action strategy within the scope of distribution. A basis for decision about strategy of distribution actions, or aiming at improvement of executed processes, is a proper identification and analysis of external and internal factors. Rationalization of conduct result in high quality of customer service in the aspect of promptness of delivery, and also affects planning of actions in the long term. The goal of the article was to analyze the logistics distribution of the enterprise acting within the scope of e-commerce and to propose a strategy to improve the execution of the process of completion. A model has been developed and simulation has been made.

Age, growth, maturity, and mortality of an understudied Gray Snapper fishery from the north‐central Gulf of Mexico

AbstractObjectiveGray Snapper Lutjanus griseus constitute a significant recreational fishery and minor commercial fishery throughout the Gulf of Mexico. The overall stock appears healthy based on the results of the most recent assessment; in fact, shifting climate conditions have led to range expansions and subsequent population growth in the north‐central Gulf of Mexico, a region for which the full suite of basic biological data is currently lacking for this species. This work presents the results of a comprehensive study on the age, growth, maturity, and mortality of Gray Snapper in Alabama and Mississippi waters.MethodsFrom 2011 to 2021, otoliths (n = 886) and gonads (n = 364) were sampled and processed according to standard microscopic techniques. Age data were used to fit three separate growth functions and derive estimates of total, natural, and fishing mortality, while testes and ovaries were categorized by reproductive phase to determine maturity at length and age.ResultAges ranged from 0 to 27 years, representing one of the most comprehensive collections across published studies to date. Sex‐specific growth curves constructed using the von Bertalanffy growth function were preferred over the Gompertz and logistic models based on Akaike information criterion and suggest that males reach a higher mean asymptotic length than females . Based on histological analyses, we estimate total length at 50% maturity to be 181 mm for males and 273 mm for females and total length at 90% maturity to be 215 mm for males and 354 mm for females. We estimate the age at 50% maturity to be <1.0 year for males and 2.0 years for females and the age at 90% maturity to be 1.4 years for males and 3.6 years for females. Mortality rates calculated using preferred estimators (Z = 0.30–0.38; M = 0.26; F = 0.04–0.12) indicate relatively low levels of exploitation in the north‐central Gulf of Mexico, with full recruitment to the fishery occurring at age 4.ConclusionBased on these results, the Gray Snapper population in the north‐central Gulf of Mexico is characterized by a relatively unexploited age structure with moderate longevity that has been subject to minimal fishing pressure. The findings from this study introduce new regional data for incorporation into assessments and provide an informed basis for future management decisions in this fishery.

Heavy metals in roadside soil along an expressway connecting two megacities in China: Accumulation characteristics, sources and influencing factors

Transportation is widely recognized as a significant contributor to heavy metal (HM) pollution in roadside soils. A better understanding of HM pollution in soils near expressways is crucial, particularly given the rapid expansion of expressway transportation in China in recent years. In this study, 329 roadside topsoil samples were collected along the Beijing-Tianjin Expressway, which connects two megacities in China. Chemical analysis showed that HM concentrations in the soil samples were generally below national limits. The mean pollution index (Pi) values for As, Cr, Cu, Ni, Pb, and Zn ranged from 0.94 to 1.01, while Cd and Hg exhibited slightly higher mean Pi values of 1.19 and 1.13, respectively. The Nemerow integrated pollution index values for all samples ranged from 0.71 to 4.97, with a mean of 1.26. This suggests a slight enrichment of HM above natural background levels, especially for Cd and Hg. Source apportionment using positive matrix factorization revealed that natural sources contributed the most to soil HMs (64.51 %), followed by agricultural sources (19.15 %), traffic sources (9.77 %), and industrial sources (6.57 %). The Shapley additive explanation analysis, based on the random forest model, identified soil organic carbon, deep soil HM content, altitude, total soil K2O, urbanization composite impact index, and total soil P as primary influencing factors. This indicates that the impact of transportation on roadside soils along the Beijing-Tianjin Expressway is currently relatively limited. The prominent influence of soil properties and altitude underscored the importance of “transport” and “receptor” in the soil HMs accumulation process at the local scale. These findings provide critical data and a scientific basis for decision-makers to develop policies for expressway design and roadside soil protection.

How does local government debt governance affect corporate credit mismatches? Evidence from implementing the New Budget Law

This study explores the impact of the New Budget Law, representing local government debt governance, on corporate credit discrepancies. Findings indicate that the New Budget Law can reduce corporate credit mismatch allocation based on the data from 2011 to 2022. Furthermore, the positive effects are achieved by reducing financing costs, increasing the availability of corporate finance, and reducing the negative impact of corporate credit mismatch. The results contribute to the restructuring of the local debt management system by providing a basis for decision-making and insights into establishing an efficient government and attaining robust economic growth.

Risk assessment for autonomous navigation system based on knowledge graph

This paper presents a knowledge graph construction framework based on the Adversarial Interpretive Structure Model (AISM), aiming to reveal the failure interdependencies of autonomous navigation assemblies. The autonomous navigation system is divided into 6 systems with 43 failure modes. The hierarchical correlations between multiple systems are quantified by establishing the distance matrix and adjacency matrix, which are defined as triples to serve the entities and attributes for the knowledge graph. The centrality metrics are applied to evaluate the importance of nodes and to assess the stability of the knowledge graph. The coordination relationships between autonomous navigation systems and the impact associations of failure modes are discussed to validate the reasonability of the proposed framework. The results indicate that The NAVTEX operation panel and The installation base of the temperature sensor are the most essential components in the hierarchical results. The positioning system has a high degree centrality and betweenness centrality, while the server system has the highest closeness centrality. The propagation path from Decrease in positioning accuracy to The installation base of the temperature sensor is investigated, providing the methodological and data basis for decision-making.

Pengaruh Strategi Bisnis dan Good Corporate Governance Terhadap Kinerja Perusahaan: Studi Empiris pada Perusahaan Industri Barang Konsumsi yang Terdaftar di Bursa Efek Indonesia Periode 2020-2022

Company performance is a decision from all agreed factors, therefore to assess company performance by increasing the company's financial performance. The basis for decision making can be seen by measuring the company's financial performance. Company performance is a measure of how well a company uses its assets and other resource to generate income. The study aims to analyze the effect of business strategy and good corporate governance on company performance. The population in this study are Consumer Goods Industry Companies in 2020-2022. The sample in this study were 62 companies obtained through purposive sampling. The data analysis method used is multiple linear regression. The result of this research show that the Business Strategy, the Board of Commissioners, and the Board of Directors influences company performance.

The Effect of Environmental Awareness on the Management of Higher Education

Higher education management is an important way to cultivate college students' awareness and behavior of ecological environment protection. The survey results show that at present, college students mainly rely on theoretical courses and lectures to receive ecological civilization education, but they lack practical links, which is different from the goal of ecological civilization education. Therefore, this paper comprehensively considers the weight of eco-environmental protection consciousness to higher education management and their relationship by using fuzzy comprehensive evaluation analysis method, which provides scientific basis for relevant decision-making. On the whole, fuzzy comprehensive evaluation analysis method can help to deeply understand the relationship between different factors and provide strong support for promoting eco-environmental protection in higher education management.

Vulnerability Evolution Mechanism of High-Speed Railway Construction System in the Goaf Site of Qinshui Coalfield

The main reason for serious consequences caused by disasters is “insufficient emergency preparedness and obvious system vulnerability”. To ensure the safe operation and maintenance of the high-speed railway in goaf sites and achieve effective hierarchical management, the vulnerability theory was introduced, based on the system approach, to explore the characteristics of hierarchical evolution. Firstly, based on the analysis of the internal and external disturbances of the high-speed railway construction system with underlying goaf areas, the vulnerability characteristic elements were defined, and a conceptual vulnerability model was proposed. Secondly, based on the literature research, accident cases, and standards and specifications, 25 factors affecting vulnerability were identified and a two-dimensional cross matrix was used to establish a vulnerability evaluation index system. Then, ISM was used to build hierarchical relationships among indicators, analyze the vulnerability characteristics of direct factors, indirect factors and essential factors, and a vulnerability evolution analysis framework was proposed. Finally, the Taiyuan–Jiaozuo high-speed railway project was selected for hierarchical and progressive vulnerability management. The study analyzed the coupling effect and evolution mechanism of various elements of the safety management system under external disturbances, identified the weak links of the high-speed railway system in the goaf site, and provided a decision-making basis for continuous dynamic optimization and emergency response technology, which complied with the strategic policy of sustainable development.

Dynamic Analysis of the Effectiveness of Emergency Collaboration Networks for Public Health Emergencies from a Systems Thinking Perspective

In recent years, public health emergencies have become frequent worldwide. In response to these complex and evolving emergencies, the organizations involved are increasingly collaborating with each other. From a systems thinking perspective, greater attention should be given to the long-term development and continuous operation of emergency collaboration systems. By time slicing the development of the COVID-19 epidemic in Wuhan, the different phases of emergency collaboration networks can be respectively established. A new method for identifying key organization nodes and different network attack strategies is proposed, assessing network effectiveness from two dimensions: efficiency and resilience. The results indicate that, compared to random attack strategies, the efficiency and resilience of these networks are significantly affected by deliberate attack strategies, underscoring the network’s sensitivity to high-importance nodes. Based on the variations in network efficiency and resilience, the effectiveness of different forms of networks are classified into four types. The pre-emergency network is categorized as resilience-focused, the mid-emergency network as efficiency-oriented, the post-emergency network as efficient-resilient, and the overall emergency network as inefficient-fragile. Analyzing forms of network effectiveness at different phases offers a deeper understanding of the operational characteristics, dynamic changes, and existing issues within emergency collaboration networks. This study provides a vital theoretical basis and practical guidance for emergency management departments and decision-makers on how to effectively improve collaboration mechanisms between different organizations.

Development of HTC-DBSCAN: A Hierarchical Trajectory Clustering Algorithm with Automated Parameter Tuning

Existing route-clustering methods often fail to identify abnormal sections or similarities between routes, mainly when working with large or long datasets. While sub-route clustering can detect regional patterns, it struggles to accurately capture the overall route structure. The present study proposes a new ship route-clustering method that enhances computational efficiency and noise recognition while addressing these limitations. We refined Automatic Identification System data via four data-cleaning processes and applied a statistical distance measurement to assess ship trajectory similarity. Dimensionality reduction was then used to facilitate clustering. The clustering of ship route similarities is non-parametric and can be applied to datasets not separated based on density to find clusters of various densities. Density-Based Spatial Clustering of Applications (DBSCA) applies to many research fields; using the DBSCA with Noise (DBSCAN) algorithm, we propose an improved DBSCAN algorithm that automatically determines the parameters Epsilon and MinPts. In this study, as a core ship route-clustering process, we propose a sub-route clustering process by setting the distance and density of data points to clear standards for re-analysis and completion. The proposed approach demonstrates markedly enhanced clustering performance, offering a more sophisticated and efficient basis for ship route decision-making.