Data Scheduling Research Articles

Patient-reported harm following cancellation of planned surgery at a Danish university hospital: a cross-sectional study.

To estimate the prevalence and severity of patient-reported physical and non-physical harm following surgery cancellation. Cross-sectional study. A large Danish university hospital. Patients (≥18 years) from various surgical specialities, such as orthopaedic, spinal, abdominal, gynaecological, thoracic, vascular and urological surgery whose surgery was cancelled <2 weeks prior to the scheduled date due to hospital-related causes.OutcomePatient-reported physical and non-physical harm, defined as physical worsening, emotional strain and other consequences, measured using a patient-reported survey. We identified 785 patients whose surgery was cancelled from 1 December 2021 to 1 June 2022, of whom 436 (55.5%) responded to the electronic survey. Physical worsening was reported by 42% and emotional strain by 48% of patients. One-third of patients reported an inability to continue daily activities, and 28% reported a need for an increased dose of analgesics. Emotional strain included various negative feelings such as being disappointed (59%) and lonely (31%). Furthermore, 44% of the respondents feared deterioration of their disease and 9% experienced anxiety of dying. The relative risk of emotional strain was higher in females than in males (54% vs 41%, adjusted relative risk (RR)=1.32 (1.08; 1.63)). A waiting period >30 days compared with ≤30 days was associated with a higher risk of physical worsening (25.3% vs 48.9%, adjusted RR=1.93 (1.42; 2.63)). Harm, measured as physical worsening and emotional strain, is reported with severity by nearly half of respondents following cancellation.

Adherence to infliximab treatment in patients with immune-mediated inflammatory diseases from a referral center in Brazil: a cohort study

BackgroundInfliximab therapy is effective in controlling symptoms and attaining clinical remission of immune-mediated inflammatory diseases. However, treatment adherence is essential to achieve the therapeutic objective. This study aimed to determine the rate of adherence to infliximab treatment in patients treated at a referral center at a university hospital.MethodThis ambispective cohort study included patients treated at the Professor Edgard Santos University Hospital (HUPES) referral center of our university hospital between March 2022 and February 2023. Sociodemographic, clinical, and pharmacotherapeutic data were collected from 101 patients through interviews and medical record reviews using a structured form. The adherence rate was defined as the proportion of days covered in a year. Patients who achieved an adherence rate > 80% were considered adherent.ResultsThe treatment adherence rate was 91.04%. Individuals with inflammatory bowel diseases had a 39.1% higher risk of non-adherence to treatment compared with other patients in our sample (p < 0,05). Most patients achieved remission or control of the underlying disease activity and had good functional capacities. The main reason for absence on the scheduled date was difficulty traveling to the referral center.ConclusionsDespite the reported difficulties, treatment adherence was observed to be high. As the study was conducted in a reference unit with multidisciplinary care and continuous monitoring for treatment effectiveness, safety, and adherence, welcoming and good communication between professionals and patients may have contributed to the high adherence rate.

Deep reinforcement learning optimization scheduling algorithm for continuous production line

In order to improve the scheduling effect of continuous production lines and enhance the processing efficiency of production lines, a deep reinforcement learning optimization scheduling algorithm for continuous production lines is proposed. The Monte Carlo algorithm and Bayesian evaluation method are combined to reduce the data complexity of the continuous production line pipeline problem; the deep neural network model is used to optimize the pipeline scheduling parameters, evaluate and encode them; the iterative greedy algorithm is combined with the deep reinforcement learning method to implement the model solution for the scheduling data problem and realize the scheduling of continuous production lines. The experimental results show that the optimal comprehensive evaluation results of the scheduling results of the proposed algorithm are all higher than 0.9531, the process delay is optimized to less than 5 min, the collection speed is fast, and the processing efficiency of the production line is improved.

Security Enhancement Scheduling Model for IoT‐Based Smart Cities Through Machine Learning Method

ABSTRACTThe Internet of Things (IoT) is rapidly evolving, and this has supported the adoption of a new computing paradigm that moves processing power to the network's edge. The job must be assigned to the computer nodes, where their associated data is available, to minimize overheads generated by data transmissions in the network, because the edge nodes have limited processing power and are vulnerable to security. Hence, the paper introduces a novel security‐enhanced scheduling model for IoT‐based smart cities utilizing machine learning techniques. Initially, nodes are initialized using the LHK‐Means algorithm. Subsequently, tasks, representing requests from multiple users to access IoT data, are scheduled. Anomaly detection tasks are then identified using an L2‐Norm‐based fuzzy model. Normal tasks are processed by the BN‐CNN model, which schedules data collection tasks through the initialized IoT device nodes. Comparative analysis with existing models illustrates the effectiveness of the proposed approach in terms of accuracy, precision, recall, sensitivity, specificity, and f‐measure.

Improving access to outpatient computed tomography

Demand for diagnostic imaging services in the United States continues to rise, posing challenges for health systems to maintain efficient scheduling processes. This study documents a quality improvement initiative undertaken at our institution in response to a surge in demand for outpatient imaging during 2022, which led to a notable scheduling backlog. By October 2022, the average scheduling interval, defined as the time from order placement to scheduled examination date, had increased from 2 weeks to 6 weeks. The objective of this initiative was to reduce the scheduling interval from 6 weeks to 10 days by January 2023. Utilizing feedback from schedulers, technologists, and radiologists, several interventions were implemented. The impact of each intervention was monitored with a control chart with weekly appointment delays tracked as a balancing measure. Initially, examination slots were double-booked for a period of 4 weeks to address the backlog, resulting in a reduction of the scheduling interval to 12 days (72 % decrease). Subsequently, examination slot duration was shorted from 20 to 15 min and contrast protocols were standardized across all sites. These adjustments further decreased the interval to 7 days (41 % reduction) over the following 9 weeks. While staffing shift adjustments had no impact on the scheduling interval, the introduction of an extra CT scanner reduced the interval to 3 days (57 % decrease). These interventions resulted in a notable increase in examination volume, from a weekly average of 722 to 860 examinations (19 % increase), approximately an additional $1,612,000 in annual revenue. Importantly, there was no change in the average appointment delay, which remained at 15 min over the study period. These improvements were sustained across the subsequent months and received favorable subjective feedback from staff. While the initiative successfully addressed scheduling inefficiencies across our health system, the rise in examination volumes has led to an increased turnaround time for completed reports. Future directions for enhancing the outpatient scheduling process include expanding online scheduling platforms, implementing systems to assess imaging appropriateness, and developing urgency stratification to prioritize time-sensitive examinations.

DESIGNING A MICROSERVICES BASED ENTERPRISE ARCHITECTURE USING TOGAF 10: A CASE STUDY APPROACH

PT Umbara Karya Sejati, operating in the home living and industrial product sectors, faced significant digital transformation challenges, marked by inefficiencies due to system integration issues and vendor dependencies that incurred cost penalties of 10-15% each month. Addressing these challenges, this research developed an Enterprise Architecture using TOGAF 10's Microservices Architecture (MSA) from the Preliminary Phase to Phase F: Migration Planning. This approach aimed to enhance system integration and operational efficiency, thereby improving modularity and scalability while reducing reliance on external vendors. A pivotal component of this architecture, an API gateway, provided robust monitoring capabilities for integration processes, enabling quick identification and prioritization of critical issues, which will assist the internal IT team's workflow. Furthermore, the research planned the establishment of a DevOps team, incorporating Agile methodologies, and scheduled IT governance and data security training to prepare for future policy development and strengthen internal controls. This strategic design equips the organization to navigate the volatility, uncertainty, complexity, and ambiguity (VUCA) of market demands with agile and effective responses, projecting a 10% increase in both operational and cost efficiency for PT Umbara Karya Sejati.

A Versatile, Machine-Learning-Enhanced RF Spectral Sensor for Developing a Trunk Hydration Monitoring System in Smart Agriculture

This paper comprehensively explores the development of a standalone and compact microwave sensing system tailored for automated radio frequency (RF) scattered parameter acquisitions. Coupled with an emitting RF device (antenna, resonator, open waveguide), the system could be used for non-invasive monitoring of external matter or latent environmental variables. Central to this design is the integration of a NanoVNA and a Raspberry Pi Zero W platform, allowing easy recording of S-parameters (scattering parameters) in the range of the 50 kHz–4.4 GHz frequency band. Noteworthy features include dual recording modes, manual for on-demand acquisitions and automatic for scheduled data collection, powered seamlessly by a single battery source. Thanks to the flexibility of the system’s architecture, which embeds a Linux operating system, we can easily embed machine learning (ML) algorithms and predictive models for information detection. As a case study, the potential application of the integrated sensor system with an RF patch antenna is explored in the context of greenwood hydration detection within the field of smart agriculture. This innovative system enables non-invasive monitoring of wood hydration levels by analyzing scattering parameters (S-parameters). These S-parameters are then processed using ML techniques to automate the monitoring process, enabling real-time and predictive analysis of moisture levels.

Not on the right rung for me? How status inconsistency leads to avoidance of status-threatening ties in NCAA.

This study examines the impact of status inconsistency on status-threatening activities within NCAA Division I men's basketball teams. Specifically, we focus on a nested form of status that includes both individual and group-level elements. We argue that organizations dealing with status inconsistency stemming from such nested form face challenges in reducing status inconsistency. To maintain their deserved status, these status-inconsistent organizations tend to avoid activities that could further threaten their status, despite potential economic gains. An analysis of NCAA Division I men's basketball scheduling data from 2000 to 2019 provides robust support to our theoretical arguments. Our findings suggest that the status inconsistency between a team's status and its conference status diminished the likelihood of scheduling games with non-Division I teams, a behavior considered counter-normative in this context. This effect is most prominent among teams in "Mid Major" conferences, while teams with recent participation in the NCAA Tournament show a mitigated effect.

A Gaussian hybrid clustering-based method for compensating for the loss of semi-persistent scheduling data in the middle station of large power grid regulation and control

In order to ensure the effectiveness of power grid scheduling decisions, ensure the stability, safety, and intelligence level of scheduling operations, and solve the semi persistent problem of station scheduling data loss in power grid scheduling, a Gaussian mixture clustering based method for compensating station scheduling data loss in power grid scheduling is proposed. Based on the role of the intermediate station and its semi persistent scheduling data generation mechanism in the control system of the large power grid, a Gaussian mixture model is constructed to calculate the conditional expected value of missing data as compensation value, and the final compensation result of the semi persistent scheduling data of the intermediate station is obtained. The experimental results show that in various types and degrees of scheduling data missing scenarios, this method performs well, and its Pearson correlation coefficient for compensating data is generally higher than 0.94, fully verifying the effectiveness and accuracy of this method. This achievement not only provides a practical and feasible solution to the problem of data loss in power grid scheduling, but also provides strong technical support for improving the accuracy of power grid regulation and ensuring the safe and stable operation of the power grid.

The Relationship Between Diet Quality and 21-Year Cumulative Health Care Costs Among Australian Women: A Longitudinal Cohort Study

BackgroundThe relationships between diet quality and health care costs have not been explored beyond 15 years. ObjectiveTo investigate relationships between both baseline diet quality and change in diet quality over time with cumulative data on health care claims and costs over 21 years among Australian women. DesignThis is a secondary analysis of data from a cohort study, the Australian Longitudinal Study on Women’s Health. Participants/SettingData for women born 1946–51 included diet quality data at baseline (2001, n = 8228), change in diet quality (2001–2013, n = 6553), and cumulative administrative health care data (2001–2021). Main OutcomesDiet quality was assessed using the Australian Recommended Food Score (ARFS) and the Fruit and Vegetable Variety Score. Twenty-one-year cumulative Medicare Benefits Schedule data (number of claims; total charges [$USD]), Australia’s universal health care coverage, were reported by baseline ARFS quintile and category of diet quality change (“diet quality worsened” [ARFS decrease ≤ –4 points], “remained stable” [–3 ≤ change in ARFS ≤ 3 points] or “improved” [ARFS increase ≥ 4 points]). Statistical AnalysisLinear regression analyses were conducted and adjusted for socioeconomic, health, and lifestyle factors. ResultsHigher baseline vegetable ARFS were correlated with fewer 21-year cumulative Medicare claims (β = –4.9, 95% confidence interval [CI], –7.3, –2.4) and charges (β = –$214; 95% CI, –$341; –$88). Baseline higher dairy scores were correlated with higher Medicare claims (β = 17.2; 95% CI, 11.1, 23.3) and charges (β = $762; 95% CI, $448, $1076). Compared with women whose diet quality score remained stable, those whose diet quality worsened over time made significantly more claims and higher charges; median (Q1, Q3) 413 (277, 588) claims, $17 868 ($11 037, $27 808) cumulative charges, compared with 387 (259, 559) claims, and $16 953 ($10 033, $26 604) cumulative charges. Change in total ARFS and ARFS subscales were predictors of 21-year cumulative health care claims. For each 1-point increase in ARFS over time, 1.2 fewer health care claims were made (95% CI, 0.3–2.2). Increasing vegetable and dairy ARFS scores were correlated with significantly fewer claims. ConclusionsBaseline greater variety of vegetables was correlated with fewer 21-year health care claims and costs. Worsening diet quality over time was correlated with greater cumulative health care claims and costs. Consideration of dietary quality and variety in national policy is suggested to potentially reduce national health care claims and costs.

Enhancing airline connectivity: An optimisation approach for flight scheduling in multi-hub networks with bank structures

While one salient characteristic of hub airports lies in connecting passengers, the full-service airlines in North America concentrate their networks spatially over a number of hubs. Having witnessed the emerging multi-hub network, this paper investigated the flight scheduling problem under a multi-hub configuration, taking a well-defined bank structure and airport operational restrictions into account. An integrated non-convex Mixed-Integer Nonlinear Programming (MINLP) approach was proposed to enhance airline connectivity, considering different combinations of traffic flow direction and connecting times. To verify the scalability and effectiveness of the proposed model, a comprehensive case study has been undertaken with real-world scheduling data from Air China, which was solved by a novel problem-specific Selective Simulated Annealing (SSA) algorithm. Substantial improvements were achieved without sacrificing the scheduling efficiency. Precisely, the programme adjusted the flights during a typical operational day in a timely manner. The post-optimisation outcomes have witnessed its effectiveness with a 17.97%, 17.06%, 22.41% and 53.86% increase in airline connectivity at its four major hub airports (Chengdu Shangliu, Beijing Capital, Shanghai Pudong and Hongqiao) in China, respectively. A clear pattern of the bank structure also confirms its positive impact on airline connectivity under the multi-hub network configuration. Lastly, a comparative analysis for the distribution of all feasible connections further highlights the critical challenge concerning the role of the hubs in a multi-hub network. More specifically, Air China’s multi-hub network systematically performs better on Domestic-International routes, due to flight schedule, frequencies, geographical placements and detours. Among the four hub airports, Beijing Capital International Airport stands out as a dominant one, which implies its potential to serve as a robust international hub airport.

A Hybrid Sparse-dense Defensive DNN Accelerator Architecture against Adversarial Example Attacks

Understanding how to defend against adversarial attacks is crucial for ensuring the safety and reliability of these systems in real-world applications. Various adversarial defense methods are proposed, which aim at improving the robustness of neural networks against adversarial attacks by changing the model structure, adding detection networks, and adversarial purification network. However, deploying adversarial defense methods in existing DNN accelerators or defensive accelerators leads to many key issues. To address these challenges, this article proposes sDNNGuard , an elastic heterogeneous DNN accelerator architecture that can efficiently orchestrate the simultaneous execution of original ( target ) DNN networks and the detect algorithm or network. It not only supports for dense DNN detect algorithms, but also allows for sparse DNN defense methods and other mixed dense-sparse (e.g., dense-dense and sparse-dense) workloads to fully exploit the benefits of sparsity. sDNNGuard with a CPU core also supports the non-DNN computing and allows the special layer of the neural network, and used for the conversion for sparse storage format for weights and activation values. To reduce off-chip traffic and improve resources utilization, a new hardware abstraction with elastic on-chip buffer/computing resource management is proposed to achieve dynamical resource scheduling mechanism. We propose an extended AI instruction set for neural networks synchronization, task scheduling and efficient data interaction. Experiment results show that sDNNGuard can effectively validate the legitimacy of the input samples in parallel with the target DNN model, achieving an average 1.42× speedup compared with the state-of-the-art accelerators.

A rolling horizon for rolling stock maintenance scheduling problem with cyclical activities

The rolling stock maintenance scheduling problem addressed here involves scheduling a set of cyclical preventive maintenance activities while considering resource capacity and geographically dispersed depots. The objective is to minimize both corrective and preventive maintenance costs using reliability features. We propose to use rolling horizon to decompose the problem into several successive subproblems such that each one tackles the scheduling of only the next cycle of activities instead of all cycles. The scheduled dates to be fixed should be as close as possible to their respective deadlines. Each train can have three status (in service, in maintenance, out of service). The activities deadlines are determined depending on component failure rate which has to be respected otherwise the train becomes out of service. An integer linear programming (ILP) model is proposed to solve the subproblem using a time oriented modeling to get an accurate maintenance cost (preventive and corrective) and respect a minimum of available trains. The rolling horizon allows to build a feasible schedule within a predefined planning horizon for moderate size problems with an interesting trade-off between computation time and solution quality.

Evaluating high-resolution aviation emissions using real-time flight data

Amidst robust global economic growth and advancing globalization, the aviation market is poised for significant expansion. Consequently, the environmental impact of aviation emissions is growing in significance. However, due to limitations in real flight data and aviation emissions index models, further clarification of the emission characteristics throughout entire flights is necessary. To better assess the emission characteristics of entire flights, this study employs real Quick Access Recorder (QAR) data and a high-precision aviation emissions index model, yielding four-dimensional emission data (time, longitude, latitude, altitude) from flights. The analysis compares QAR data with emissions from scheduled flight data (SFD) and Broadcast Automatic Correlation Monitoring (ADS-B) projections, explores seasonal variations in aviation emissions, and assesses the impact of sustainable aviation fuels (SAFs) on emissions reductions. For both number and mass of nvPM emissions, as well as nitrogen oxide emissions, the rankings are: ADS-B-E > SFD-E > QAR-E; for CO, SFD-E > ADS-B-E > QAR-E, particularly during the climb-cruise-descent (CCD) cycle. There are significant differences in the emission of aviation pollutants in airport area and high-altitude area in different seasons. Employing four types of Sustainable Aviation Fuels (SAFs) significantly reduces both the mass and the number of nvPM emissions. Therefore, it is recommended to utilize more QAR data to refine the assessment of the environmental impact of aviation emissions.

Secure Patient Data Monitoring and Efficient Routing Optimization using a Hyperelliptic Curve Cryptography with Fuzzy-based Priority in WBSN

Aims and Background: Wireless Body Sensor Network (WBSN) technology is one of the major research areas in the medical and entertainment industries. A wireless sensor network (WSN) is a dense sensor network that senses environmental conditions, processes, and outgoing data at the sink node. A WBSN develops patient monitoring systems that provide the flexibility and mobility needed to monitor patient health. In data communications, it is difficult to find flexible optical routing paths, switching capabilities, and packet processing in the composition of optical networks. Information-centric networks (ICNs) are a new network model and are different from information- centric models. The priority of the information-centric model is the communication network. Objectives: In the existing literature, such methods are typically developed using computationally expensive procedures, such as bilinear pairing, elliptic curve operations, etc., which are unsuitable for biomedical devices with limited resources. Using the concept of hyperelliptic curve cryptography (HECC), we propose a new solution: a smart card-based two-factor mutual authentication scheme. In this new scheme, HECC’s finest properties, such as compact parameters and key sizes, are utilized to enhance the real-time performance of an IoT-based TMIS system. Methodology: A fuzzy–based Priority Aware Data Sharing (FPADS) method is introduced to schedule the priority data and monitor the transmission length. The child node adjusts the transmission speed of the cluster head with the help of a fuzzy logic controller (FLC). Results: The proposed model estimated the traffic load of the child node and the priority of the different amounts of data to be transmitted. The principle of scheduling data packets to be developed is based on the precedence of the data with the lowest transmit length in the network. Conclusion: The proposed FPADS performance increases in terms of scheduling time utilisation, traffic distribution, and mean delay. Simulations have been done using NS2, and the outcomes have shown that the proposed methodology is efficient and improves the overall QoS of the system.

Aplikasi Absensi dan Penjadwalan Berbasis Web di SMA Negeri 1 Pelaihari menggunakan framework Laravel

Pelaihari 1 Public High School, located in Pelaihari District, is one of the educational institutions that supports improving the quality of education. Providing information by students is carried out by collecting the necessary data, then the data is input using Microsoft Excel or Microsoft Word. Apart from that, paper is also used when making attendance. Any existing data is then shared back with teachers and students who need it via WhatsApp. This method is considered less effective due to limited program functions, easy file loss, difficulty in searching for data, and also in terms of time. Therefore, a Web-Based Attendance and Scheduling Application was developed at SMA Negeri 1 Pelaihari using the Laravel Framework. This application is expected to fulfill one of the Admin's tasks in managing data, so that Admins feel it is easier to share information with teachers and students. The features contained in this system include class data, student data, employee data, subject data, schedule data, User data, attendance data, reporting and journal data. This system was developed using a prototype development model, the Laravel framework, and was designed using the Unified Modeling Language (UML) and Entity Relationship Diagram (ERD) models. The programming language used is PHP and the database used is MySQL.

Operating Room Scheduling via Answer Set Programming: improved encoding and test on real data

Abstract The Operating Room Scheduling (ORS) problem deals with the optimization of daily operating room surgery schedules. It is a challenging problem subject to many constraints, like to determine the starting time of different surgeries and allocating the required resources, including the availability of beds in different units. In the past years, Answer Set Programming (ASP) has been successfully employed for addressing and solving the ORS problem. Despite its importance, due to the inherent difficulty of retrieving real data, all the analyses on ORS ASP encodings have been performed on synthetic data so far. In this paper, first we present a new, improved ASP encoding for the ORS problem. Then, we deal with the real case of ASL1 Liguria, an Italian health authority operating through three hospitals, and present adaptations of the ASP encodings to deal with the real-world data. Further, we analyse the resulting encodings on hospital scheduling data by ASL1 Liguria. Results on some scenarios show that the ASP solutions produce satisfying schedules also when applied to such challenging, real data.1

Overnight Electroencephalogram to Forecast Epilepsy Development in Children with Autism Spectrum Disorders

ObjectiveTo establish the utility of long-term electroencephalogram (EEG) in forecasting epilepsy onset in children with autism spectrum disorder (ASD). Study designA single-institution, retrospective analysis of children with ASD, examining long-term overnight EEG recordings collected over a period of 15 years, was conducted. Clinical EEG findings, patient demographics, medical histories, and additional Autism Diagnostic Observation Schedule (ADOS) data were examined. Predictors for the timing of epilepsy onset were evaluated using survival analysis and Cox regression. ResultsAmong 151 patients, 17.2% (n=26) developed unprovoked seizures (Sz group), while 82.8% (n=125) did not (non-Sz group). The Sz group displayed a higher percentage of interictal epileptiform discharges (IEDs) in their initial EEGs compared with the non-Sz group (46.2% vs. 20.0%, p=0.01). The Sz group also exhibited a greater frequency of slowing (42.3% vs. 13.6%, p < 0.01). The presence of IEDs or slowing predicted an earlier seizure onset, based on survival analysis. Multivariate Cox proportional hazards regression revealed that the presence of any IEDs (HR 3.83, 95% CI 1.38-10.65, p=0.01) or any slowing (HR 2.78, 95% CI 1.02-7.58, p=0.046 significantly increased the risk of developing unprovoked seizures. ConclusionLong-term EEGs are valuable for predicting future epilepsy in children with ASD. These findings can guide clinicians in early education and potential interventions for epilepsy prevention.

Pengembangan Sistem Informasi Penjadwalan dan Monitoring Backup Data Berbasis Web

This research aims to facilitate the admins of the Business Partner Department and IT Services at PT. Pupuk Sriwidjaja Palembang in searching for data, backing up data, and scheduling data in an organized manner. To support this convenience, the author has developed a web-based infrastructure backup data portal system, utilizing a MySQL database. This system can be used to manage network data, website data, as well as data reports or archives. The research method employed is the Waterfall method, which consists of several stages: analysis, design, implementation, testing, and maintenance. Data collection methods included interviews and observations. The result of this research is a website that enhances the performance of the admins of the Business Partner Department and IT Services at PT. Pupuk Sriwidjaja Palembang in managing company data.

A Novel Technical Framework for the Evaluation of Node Significance and Edge Connectivity in Global Shipping Network

Marine transportation is pivotal in the rapid development of global trade, significantly enhancing international economic and trade connectivity and impacting the sustainable development of the global economy. In this study, we developed a novel technical framework based on the Laplacian matrix to evaluate the node significance and edge connectivity of the global shipping network using 2019 vessel schedule data from the top 30 liner shipping companies, as ranked by Alphaliner. Our analyses were conducted in both L-space, characterizing the connectivity function, and P-space, characterizing the transfer function. The findings indicate the following. (1) There is no consistent relationship between node significance and centrality for most ports. Ports with high node significance are mainly located in the Asia–Pacific region, with Singapore Port being the port with the highest node significance in L-space and Shanghai Port being the port with the highest node significance in P-space. (2) In L-space, the structures with significant improvements in edge connectivity in the shipping network have at least one port node that exhibits both low degree centrality and low betweenness centrality; these are primarily found on East African routes. (3) In P-space, the structures with significant improvements in edge connectivity in the shipping network are more complex but are notably linked to the ports of Assaluyeh and Bandar Abbas in Iran. The proposed node evaluation and edge addition strategy effectively analyze port significance and edge connectivity, providing decision-making support for optimizing port layouts, supporting container route planning, and enhancing the overall performance of the shipping network.