Location Of Nodes Research Articles

Acoustic Sensors data transmission integrity and endurance with IoT-enabled location-aware framework

Environmental monitoring and disaster mitigation are critical applications of underwater acoustic sensor networks (UASNs). However, UASNs face significant challenges, including high latency, limited bandwidth, and energy constraints. This study introduces an Internet of Things (IoT)-driven location-aware framework (ILAF) designed to enhance UASN performance by utilizing non-GPS geographic coordinates for determining the location of sensor and sink nodes, identifying their neighbors based on coordinates and transmission range, and optimizing node placement and routing without the need for GPS modems. The framework is compared with several state-of-the-art protocols, including Bald Eagle Search inspired optimized energy efficient routing protocol (BES-OEERP) and IoT-enabled depth-based routing technique (IDBR), demonstrating superior performance. Specifically, ILAF achieved a packet delivery ratio (PDR) of 99%, which outperforms energy-efficient region-based source distributed routing algorithm (EERSDRA) (98%) and energy-efficient geo-opportunistic routing protocols (EEGORP) (96%). Additionally, ILAF reduced energy consumption by 20% compared to these existing protocols. These improvements result in a more energy-efficient network with fewer dead nodes (12 after 1,000 rounds) and higher throughput (5.7 kbps at 1,000 rounds), making ILAF suitable for real-time underwater applications. Future research will explore integrating lightweight IoT protocols like Message Queue Telemetry Transport (MQTT) and Constrained Application Protocol (CoAP) to enhance the framework’s performance and reliability further.

Comparative evaluation of negative lymph node count, positive lymph node count, and lymph node ratio in prognostication of survival following completely resection for non-small cell lung cancer: a multicenter population-based analysis

ObjectiveLung cancer is the leading cause of cancer-related mortality. Lymph node involvement remains a crucial prognostic factor in non-small cell lung cancer (NSCLC), and the TNM system is the current standard for staging. However, it mainly considers the anatomical location of lymph nodes, neglecting the significance of node count. Metrics like metastatic lymph node count and lymph node ratio (LNR) have been proposed as more accurate predictors.MethodsWe used data from the SEER 17 Registry Database (2010–2019), including 52,790 NSCLC patients who underwent lobectomy or pneumonectomy, with at least one lymph node examined. Primary outcomes were overall survival (OS) and cancer-specific survival (CSS). Cox regression models assessed the prognostic value of negative lymph node (NLN) count, number of positive lymph node (NPLN), and LNR, with cut-points determined using X-tile software. Model performance was evaluated by the Akaike information criterion (AIC).ResultsThe Cox proportional hazards model analysis revealed that NLN, NPLN, and LNR are independent prognostic factors for OS and LCSS (P < 0.0001). Higher NLN counts were associated with better survival (HR = 0.79, 95% CI = 0.76–0.83, P < 0.0001), while higher NPLN (HR = 2.19, 95% CI = 1.79–2.67, P < 0.0001) and LNR (HR = 1.64, 95% CI = 1.79–2.67, P < 0.0001) values indicated worse outcomes. Kaplan-Meier curves for all three groups (NLN, NPLN, LNR) demonstrated clear stratification (P < 0.0001). The NLN-based model (60,066.5502) exhibited the strongest predictive performance, followed by the NPLN (60,508.8957) and LNR models (60,349.4583), although the differences in AIC were minimal.ConclusionsNLN count, NPLN, and LNR were all identified as independent prognostic indicators in patients with NSCLC. Among these, the predictive model based on NLN demonstrated a marginally superior prognostic value compared to NPLN, with NPLN outperforming the LNR model. Notably, higher NLN counts, along with lower NPLN and LNR values, were consistently associated with improved survival outcomes. The relationship between these prognostic markers and NSCLC survival warrants further validation through prospective studies.

Organ-preserving laparoscopic myomectomy in atypical leiomyoma arrangement

BACKGROUND: Uterine fibroids are the most common cause of surgical treatment in women and total hysterectomy is performed in most cases to radically solve this problem. The organ-preserving approach aims to preserve fertility and the ability to reproduce in patients of reproductive age. The atypical location of leiomyomas complicates the possibility of performing an organ-preserving myomectomy. AIM: The aim of this study was to assess the possibility of performing myomectomy for atypical leiomyoma using laparoscopic access in routine practice to preserve fertility and implement reproductive plans. MATERIALS AND METHODS: This study included 213 patients of reproductive age with uterine fibroids. Myomectomy for typical or atypical leiomyomas was performed by laparoscopic access. A comparative analysis of surgical treatment and perioperative management of patients with atypically located fibroids and fibroids of other localization was carried out. The results of the achievement by the patients of their reproductive goals were evaluated. The duration of follow-up was 24 months. RESULTS: Atypical node location was noted in 65 (30.5%) cases. When comparing the duration of surgical treatment, volume of blood loss, need for analgesics, presence of intraoperative and postoperative complications, duration of hospitalization and recovery period in patients with atypical leiomyoma, these parameters did not differ from those in the surgical treatment of patients with typical leiomyoma. Anatomical and functional results of surgical treatment were analyzed after one, three, six, 12, and 24 months. Of the 70 patients planning pregnancy, 26 (37%) women became pregnant on average 8.3 months after surgery. The location of the removed fibroids was atypical in ten cases. CONCLUSIONS: The atypical location of leiomyoma does not affect the course of the surgical and postoperative periods and long-term results of surgical treatment. The possibility of routine organ-preserving treatment by laparoscopic access in patients with atypical fibroids leaves prospects for the implementation of the woman’s reproductive plans.

Accurate Range-Free Localization Using Cuckoo Search Optimization in IoT and Wireless Sensor Networks

Precise positioning of sensors is critical for the performance of various applications in the Internet of Things and wireless sensor networks. The efficiency of these networks heavily depends on the precision of sensor node locations. Among various localization approaches, DV-Hop is highly recommended for its simplicity and robustness. However, despite its popularity, DV-Hop suffers from significant accuracy issues, primarily due to its reliance on average hop size for distance estimation. This limitation often results in substantial localization errors, compromising the overall network effectiveness. To address this gap, we developed an enhanced DV-Hop approach that integrates the cuckoo search algorithm (CS). Our solution improves the accuracy of node localization by introducing a normalized average hop size calculation and leveraging the optimization capabilities of CS. This hybrid approach refines the distance estimation process, significantly reducing the errors inherent in traditional DV-Hop. Findings from simulations reveal that the developed approach surpasses the accuracy of both the original DV-Hop and multiple other current localization methods, providing a more precise and reliable localization method for IoT and WSN applications.

Optimization of Extended Pelvic Lymph Node Dissection Side for Prostate Cancer.

This study aimed to show the association between tumor location and laterality of positive lymph nodes by evaluating biopsy and magnetic resonance imaging (MRI) findings, and to optimize the extended pelvic lymph node dissection (ePLND) side for prostate cancer. The study enrolled patients who underwent robot-assisted radical prostatectomy with ePLND. Tumor locations were determined according to International Society of Urological Pathology grade group 4/5 in biopsies and Prostate Imaging-Reporting and Data System category 4/5 in MRI results. The concordance of tumor location lobe and positive lymph node side with the performance of tumor location-guided ePLND for positive lymph node detection was evaluated. For 301 patients who underwent ePLND at Kyushu University Hospital, tumor locations determined by biopsy and MRI findings showed no lesion in 8 (2.7%) patients, unilateral lobe in 223 (74.1%) patients, and bilateral lobe in 70 (23.3%) patients. The accuracies for detection of any and all positive lymph nodes by tumor location-guided unilateral ePLND were 99.6% and 97.3%, respectively. Among the patients at St. Luke's International Hospital, the accuracies for detection of any and all positive lymph nodes by tumor location-guided unilateral ePLND were estimated to be 99.0% and 97.3%, respectively. This study proposed tumor location-guided ePLND according to biopsy and MRI findings. This novel strategy is expected to reduce the burden of bilateral ePLND at the cost of acceptable risk of failing to detect positive lymph nodes.

Lymph Node Evolution in Eyelid and Orbit Squamous Cell Carcinomas.

To describe a large cohort of eyelid and periorbital SCCs, to compare the location of the tumor and of the pathological lymph nodes, and to analyze the risk factors for lymph node involvement among tumor characteristics. All patients managed inside our institution for an eyelid and periorbital SCCs were included. Tumor characteristics, imaging setup, excision margins, lymph node evolution features, local, regional, and distant recurrences rates, and global survival were reported. The risk for lymph node involvement and location of pathological lymph nodes were analyzed through univariate and multivariate analyses. Between January 2012 and August 2022, 115 patients were included, and 18 presented a lymph node evolution (15.7%), involving the parotid gland in 16 cases (88.9%), the submental and submandibular areas in seven cases (38%), and the jugular and carotid areas in four cases (22%). Tumor size above 20 mm, infiltration of the external canthus and periorbital structures, the presence of perineural invasion or vascular embolism, the depth of infiltration, and the presence of a local recurrence were significantly associated with the risk of lymph node evolution. Periorbital and eyelid SCCs present a true potential for lymph node evolution especially through the parotid gland. Extension setup including the parotid gland and neck should be mandatory, and lymph node dissection should be associated in case of parotidectomy for lymph node involvement. 4 Laryngoscope, 134:4956-4963, 2024.

Statistical Analysis of Complex Shape Graphs.

This paper provides developments in statistical shape analysis of shape graphs, and demonstrates them using such complex objects as Retinal Blood Vessel (RBV) networks and neurons. The shape graphs are represented by sets of nodes and edges (articulated curves) connecting some nodes. The goals are to utilize nodes (locations, connectivity) and edges (edge weights and shapes) to: (1) characterize shapes, (2) quantify shape differences, and (3) model statistical variability. We develop a mathematical representation, elastic Riemannian metrics, and associated tools for shape graphs. Specifically, we derive tools for shape graph registration, geodesics, statistical summaries, shape modeling, and shape synthesis. Geodesics are convenient for visualizing optimal deformations, and PCA helps in dimension reduction and statistical modeling. One key challenge lies in comparing shape graphs with vastly different complexities (in number of nodes and edges). This paper introduces a novel multi-scale representation to handle this challenge. Using the notions of (1) "effective resistance" to cluster nodes and (2) elastic shape averaging of edge curves, it reduces graph complexity while retaining overall structures. This allows shape comparisons by bringing graphs to similar complexities. We demonstrate these ideas on 2D RBV networks from the STARE and DRIVE databases and 3D neurons from the NeuroMorpho database.

Improved DV-Hop algorithm based on geometric Brownian motion model under communication interference

Abstract In a mobile sensor network, a traditional positioning algorithm is unable to locate unknown nodes when losing anchor positions caused by communication interference. To solve this problem, an improved DV-Hop algorithm based on a geometric Brownian motion (GBM) model was proposed including two main stages: location of sink node (LSN) and location of blind node (LBN). In the LSN stage, if the signal transmission of anchors is normal, the GBM model records the moving positions of the anchors. If not, the GBM model predicts the estimated average positions of the anchors using recorded data. Then, the trial count of the GBM model is optimized to further improve the prediction accuracy and computational overhead. In the LBN stage, the unknown nodes’ positions are obtained by the DV-Hop algorithm. In a traditional DV-Hop algorithm, the approximate minimum hop number and average hop distance may lead to huge deviation between true position and estimated position. To improve the positioning accuracy in the LBN stage, the strategies of multi-communication radius and hop distance weighting were adopted. The simulation results demonstrated that the proposed algorithm has the capability to resist communication interference and adaptability at different node speeds , maintaining a relatively high accuracy in locating unknown nodes.

Rumor detection model with weighted GraphSAGE focusing on node location

While social media platforms promote people’s information exchange and dissemination, they also make rumors spread rapidly on online platforms. Therefore, how to detect rumors quickly, timely and accurately has become a hot topic for scholars in related fields. Traditional deep learning models ignore the relationship and topology between nodes in the rumor detection task and use fixed weights or mean aggregation strategies in the feature aggregation process, which fail to capture the complex interactions between nodes and the dynamics of information propagation, limiting the accuracy and robustness of the rumor detection model. To address these problems, we propose a location-aware weighted GraphSAGE rumor detection model GSMA. We first introduce an attention mechanism that dynamically assigns different attention weights to different neighboring nodes for different degrees of aggregation, improving GraphSAGE’s strategy of using mean-value aggregation for all neighboring nodes during the aggregation process; second, we introduce a modulated position encoding into the model and encode the position information of nodes into the features to improve the model’s ability to perceive the relative position and order of nodes; finally, the post text sentiment is incorporated into the features to provide additional semantic information for the model as a way to achieve rumor detection in microblogging platforms. Experiments show that the accuracy of the GSMA model on Ma-Weibo and Weibo23 reaches 97.43% and 97.55%, which is an improvement of 1.11% and 0.77% compared to the benchmark GraphSAGE, and all the evaluation metrics are also improved compared to other optimal rumor detection models.

Risk of second primary cancers in nodal non-Hodgkin lymphoma patients by primary lymph node location: a retrospective cohort population-based study.

Non-Hodgkin lymphoma (NHL) is a diverse group of blood cancers with increasing incidence and survival rates due to advancements in treatment and early detection. However, NHL survivors are at significant risk of developing second primary cancers, which can adversely impact their long-term survival. This retrospective population-based cohort study utilized data from the Surveillance, Epidemiology, and End Results database, covering 17 geographic areas in the United States from 2000 to 2021. The authors included patients diagnosed with nodal NHL as a first primary cancer and excluded those diagnosed at autopsy or via death certificate only. Standardized Incidence Ratios, Absolute Excess Risks, and Person-Years at Risk were calculated to evaluate the risk of developing SPCs according to the primary lymph node site and stratified by latency periods following the initial NHL diagnosis. The cohort included 54012 NHL patients. The authors' results showed that for most SPCs, the risk of development was different for different primary NHL lymph node locations. The highest risks were observed for thyroid cancer, acute myeloid leukemia, and Hodgkin lymphoma. Notably, the risk for thyroid cancer was highest in the first year post-diagnosis, while hematological malignancies such as acute myeloid leukemia and Hodgkin lymphoma showed elevated risks in the intermediate and late latency periods. NHL survivors are at an increased risk of developing SPCs, influenced by the primary lymph node site and latency period. These findings highlight the need for tailored surveillance strategies and preventive measures to mitigate the long-term risks of SPCs in NHL survivors. Further research is necessary to elucidate the underlying mechanisms and to develop targeted interventions for this high-risk population.

Prevalance of incessant form of atrial fibrillation by superior vena cava (svc) firing:simplified svc and sinus node mapping(sss map) to avoid sinus node injury

Abstract Background The superior vena cava (SVC) is known as a major source of atrial fibrillation(AF), non-pulmonary vein AF foci (NPVAF). In some cases, an incessant form of AF (iAF) by SVC firing prevents obtaining the sinus node location, and anatomical SVC isolation has a risk of sinus node injury (SNI). Objective This study was aimed to explore the frequency of iAF by SVC firing and the efficacy of the simplified mapping strategy to avoid SNI. Methods Consecutive AF ablation procedures in the 3 centers were retrospectively investigated including the NPVAF prevalence. Simplified SVC/SN mapping (SSS-map: Obtaining the top-end of the SVC potential and >4points with 5-10mm spacing in the upper lateral side of the right atria (RA) for roughly locating the earliest activation site in sinus rhythm) was performed before PV isolation in the last 138 patients. In case of the initial rhythm at the procedure was atrial fibrillation, cardioversion was performed for obtaining the SSS-map. In the cases in which the high-density mapping catheters were available, the high-density activation maps of the RA in sinus rhythm were obtained to investigate the dislocation of the earliest activation site of the SSS map from the SN location. When SVC isolation was required due to iAF, SVC isolation line was designed based on the location of the top-end of the SVC potential during AF and ablative linear lesions were created to keep the distance of 5-10 mm above the second earliest activation site. Results A total of 1160 procedures in 1089 patients (male: 736[63.4%], age: 69.9, paroxysmal AF: 544[49.9%], first session: 997[85.9%]) was investigated. Prevalence of NPVAF was revealed in 160 of the all procedures (13.7%) (the SVC: 64, the posterior wall of the left atria: 24, the coronary sinus: 19, and others: 53). 16 of 160 NPVAFs (10.0%) developed to iAF by SVC firing. The SSS-map was successfully obtained in all of the last 138 patients (Initial rhythm at the procedure: AF 43.7%, Mapping time: 1.3±0.6 min, number of obtained points: 8.4±1.3). In 21 patients in which the high-density RA map was obtained, the SN dislocation distance was 5.1±2.2mm between the SSS map and the high-density RA map. Five of 138 developed to iAF by SVC firing, and SVC isolation based on the SSS-map was successfully achieved without SNI in the cases. Conclusion This study indicated iAF driven by SVC firing after PV isolation occurred in certain number of patients. The SSS-map before PV isolation was practical and showed the potential to be the mapping strategy avoid the SN injury in such cases.

The interchange stations siting model of urban rail transit network

The "node anchor weaving method" is commonly used in urban rail transit network planning, which emphasizes identifying the important nodes of the network first, and then using the key nodes as the "anchor nodes" to construct the network. Interchange stations are the most crucial network nodes in terms of both service function and network function. As a result, when planning the network, it is critical to identify the interchanges ahead of time. A key node identification model is initially established in this article, and then a network interchange stations location model is built on top of it. Based on the urban road network, the key node identification model takes into account the location of the road network nodes, the number of neighboring nodes, the influence of the neighboring nodes, and the path information between the nodes, and the key nodes in the network are effectively identified through the improvement of the gravitational model and the neighboring core model, and the model's validity is verified through the SIR contagion model. The online network interchange stations siting model considers the matching degree of residents' traffic demand, the interchange stations coverage to the city, the intensity of land use, and the interchange stations' duplicate coverage to the urban space as evaluation indices for the interchange stations; the objective of solving the interchange stations is the comprehensive optimality of the indexes; in order to avoid the problem of perturbation in the optimization and solution algorithm, this paper presents an improved genetic-simulated annealing optimization algorithm that introduces the concept of community, and avoids the problem of perturbation in the optimization and solution algorithms. The effectiveness of the model is verified by using examples of the Mitte-Center and Friedrichshain networks.

Advanced spatial query processing in IoT through mobile agent integration

In IoT environments, various applications and protocols rely on spatial query processing to obtain, analyze, and interpret information from sensor nodes based on their location within a specific area. It enables the efficient monitoring and management of spatial data, which is crucial for applications such as environmental monitoring, smart cities, and disaster response. By querying the location and status of sensor nodes, spatial query processing facilitates real-time decision-making, optimizes resource allocation, and enhances the overall performance of the IoT system.This paper focuses on Window queries, a key spatial query type that retrieves data from sensors within a defined two-dimensional zone. The research aims to improve spatial query processing in IoT environments by proposing a Window query processing method that balances various performance factors such as energy efficiency, latency, accuracy, and query success rate. We introduce a novel approach using mobile agents to enhance spatial query processing by addressing the constraints of sensor networks. Simulation results from the NS-2 simulator demonstrate the effectiveness of our method across different parameters and environments.

Graph Neural Network-Based Node Deployment for Throughput Enhancement.

The recent rapid growth in mobile data traffic entails a pressing demand for improving the throughput of the underlying wireless communication networks. Network node deployment has been considered as an effective approach for throughput enhancement which, however, often leads to highly nontrivial nonconvex optimizations. Although convex-approximation-based solutions are considered in the literature, their approximation to the actual throughput may be loose and sometimes lead to unsatisfactory performance. With this consideration, in this article, we propose a novel graph neural network (GNN) method for the network node deployment problem. Specifically, we fit a GNN to the network throughput and use the gradients of this GNN to iteratively update the locations of the network nodes. Besides, we show that an expressive GNN has the capacity to approximate both the function value and the gradients of a multivariate permutation-invariant function, as a theoretic support to the proposed method. To further improve the throughput, we also study a hybrid node deployment method based on this approach. To train the desired GNN, we adopt a policy gradient algorithm to create datasets containing good training samples. Numerical experiments show that the proposed methods produce competitive results compared with the baselines.

Achieving Reliability of Privacy-preserving Phantom Routing Protocols in Multi-hop Wireless Sensor Networks

Due to the open nature of wireless channels and sensor node resource constraints, it is challenging to secure the communication in wireless sensor networks (WSNs) while simultaneously protecting the privacy of node location data. Therefore, a significant amount of research focusing on source location privacy (SLP) protocols has been conducted. The amount of research on SLP reliability, meanwhile, is insignificant. This study explores the operational features of various privacy-preserving phantom routing protocols and simulates WSNs with varied network configurations to investigate how different routing strategies affect SLP reliability. Safety period and capture ratio metrics are used to compute SLP reliability. Simulation results show that integration of phantom routing with fake packet distribution mechanisms adversely impacts SLP reliability. SLP reliability decreases also as the number of fake packet sources increases. Research proves that a protocol with many fake packet sources achieves SLP reliability for a mission duration of 940 rounds, while a protocol with no fake packet sources achieves SLP reliability for 1658 rounds.

Decentralized Mechanism for Edge Node Allocation in Access Network: An Experimental Evaluation

With the rapid advancement of the Internet of Things and the emergence of 6G networks in smart city environments, a growth in the generation of data, commonly known as big data, is expected to consequently lead to higher latency. To mitigate this latency, mobile edge computing has been proposed to alleviate a portion of the workload from mobile devices by offloading it to nearby edge servers equipped with appropriate computational resources. However, existing solutions often exhibit poor performance when confronted with complex network topologies. Thus, this paper introduces a decentralized mechanism aimed at determining the locations of network edge nodes in such complex network topologies, characterized by lengthy execution times. Our proposal provides performance improvements and offers scalability and flexibility as networks become more complex. Experimental evaluations are conducted using the Shanghai Telecom dataset to validate our proposed approach.

Three-dimensional anatomy of a Cretaceous river avulsion

Abstract River avulsions are fundamental mechanisms underlying the construction of fluvial stratigraphy. Although avulsions occur in three dimensions (3-D), interpretations of avulsion deposits in the terrestrial stratigraphic record remain largely based on two-dimensional (2-D) lithologic stacking patterns with little spatial context of the avulsion node location. Moreover, though the genesis of intersecting fluvial ridges observed on Mars is commonly attributed to avulsion, little is known about the history or stratigraphic architecture of these intersections nor the duration of the martian fluvial activity they represent. Here, we analyze a fluvial ridge intersection in the Cretaceous Cedar Mountain Formation of eastern Utah, USA, that we interpret to reflect the evolution of a river channel avulsion node exposed in 3-D. Through integration of digital outcrop models and field characterization, we document recognition criteria for avulsion nodes and reconstruct pre-, intra-, and post-avulsion channel evolution at the node, identifying evidence of channel-bed aggradation and pre-avulsion superelevation. Using a new 3-D stratigraphic modeling approach, we simulate the avulsion and analyze the resultant synthetic stratigraphy in parallel with outcrop exposures to link avulsion processes to stratigraphic products. Our results promote recognition and process-based interpretation of ancient river avulsion deposits observed in cross section and plan view on Earth and Mars and constrain the period of fluvial activity represented by the intersection to the order of centuries rather than more significant geologic time, as has been hypothesized previously.

A transfer learning based patent transfer prediction model: Discussion about the condition of China and U.S.

In the same patent classification, there are significant differences in the patent transfer quantity for different countries. For the countries with small patent transfer quantities, the performance of the patent transfer prediction is worse than that of high-transfer countries. To improve the performance of the patent prediction model learned for the low-transfer countries, we propose a transfer learning based patent transfer prediction model, for which the high-transfer countries are the source domain and the low-transfer countries are the target domain. In the model, the textual feature of each patent text is embedded using BERT-CNN with the parameters fine-tuned on the target domain from the source domain. The textual features are optimized to minimize the domain loss of Maximum Mean Discrepancy between source and target domain. What is more, we extract graph feature vectors by embedding the patent node in a three-layer heterogeneous network which consists of patent nodes, International Patent Classification nodes, consumer nodes, consumer location nodes and their relations. Finally, the patent text vectors, structural feature vectors, and graph feature vectors are spliced together to predict target domain patents transfer or not. The performance of the model is verified by some comparative experiments. The experimental results show that the proposed model outperforms significantly the baseline models in the values of Precision, Recall, and F1 of patent data improve by 6.47%, 6.54%, and 6.58% on average, respectively.

Renormalization of networks with weak geometric coupling.

The renormalization group is crucial for understanding systems across scales, including complex networks. Renormalizing networks via network geometry, a framework in which their topology is based on the location of nodes in a hidden metric space, is one of the foundational approaches. However, the current methods assume that the geometric coupling is strong, neglecting weak coupling in many real networks. This paper extends renormalization to weak geometric coupling, showing that geometric information is essential to preserve self-similarity. Our results underline the importance of geometric effects on network topology even when the coupling to the underlying space is weak.

Patient´s perception of spatial complexity in emergency spaces of a hospital in China: a method for stakeholders to improve design and management

ABSTRACT The focus of medical facility users has shifted from prioritizing epidemic prevention to prioritizing experience after COVID-19, particularly in the emergency department (ED) of a large-scale Chinese hospital. The current visual elements of the emergency department provide a negative perception to the patients who visit the department. The study aims to optimize the ED's spatial design by understanding how visual elements affect patients' perception of spatial complexity. The methodology involved recreating a patient's typical path through the ED using data from the hospital information system (HIS), capturing images at 70 location nodes, and calculating the fractal dimensions of these images using the box-counting method. Visual elements were categorized into eight groups, and their fractal dimensions and proportions within the images were analyzed. A breakpoint graph was developed to track changes in spatial complexity perception as patients move through the ED. Statistical analysis, including correlation and one-way ANOVA, revealed that spatial attributes and lighting conditions significantly impact spatial complexity. From the results, grouping analysis was conducted on the visual elements of the horizontal surfaces, vertical surfaces, ceiling, furniture, and vegetation. Further optimization was proposed. In conclusion, this study highlights the importance of visual elements in emergency spaces, directly influencing users' perception of spatial complexity. The relationship between spatial complexity and fractal dimension is further emphasized. Stakeholders and designers can use the findings of this study as references to make informed decisions to improve the user's experience in the healthcare-built environment.