Distance Approach Research Articles

A geometric modelling approach to estimate apple fruit size by means of LiDAR 3D point clouds

ABSTRACT Remote sensing in agriculture aims to search new methods to monitor fruit at the tree, thus improving the estimation of yield-related variables. Light detection and range (LiDAR) scanning was introduced to obtain geometric and radiometric information from fruit surfaces by means of 3D point clouds. A geometric model to estimate apple size by means of segmented 3D point cloud of fruit is proposed in the present study. The model consists in the approximation of 3D point clouds to a reference shape given by 2D Fourier series expansion. Each point cloud was approximated to its reference shape using an iterative error minimization routine. The geometric model was applied to laboratory and field data of spheres and apples during fruit growth, ranging from 60 to 151 days after full bloom (DAFB). An overall RMSE between measured and predicted fruit radius of 20.1, 76.8, and 119.1% was found for the geometric model, mean, and maximum Euclidean distance approaches, respectively, including all studied growth periods in field conditions. Moreover, the linear regression on measured and predicted values showed considerably improved coefficient of determination ( R 2 ) of the geometric model in comparison to Euclidean distance calculations with R 2 values of 0.76 and 0.49 for laboratory and field scanned apples, respectively. The data processing method enables fruit monitoring and their application of terrestrial LiDAR sensing in precise orchard management.

A Fuzzy Minkowski Distance-based fusion of convolutional neural networks for gastrointestinal disease detection

Accurate classification of gastrointestinal (GI) conditions from medical images is a critical task for facilitating timely diagnosis and effective treatment. However, the reliance on manual diagnosis introduces the possibility of human errors. In response, researchers have been tirelessly working to develop robust computerized methods that can significantly enhance diagnostic accuracy. In this study, we present a novel approach designed to elevate the precision of classification tasks through the utilization of a Fuzzy Minkowski Distance-based Ensemble Model. Our ensemble model is accurately constructed by integrating three well-established pre-trained convolutional neural network (CNN) models: MobileNet, ResNet101V2, and Xception. To enhance the robustness of these base models, we incorporate ResNeXt block, effectively amplifying their ability in feature extraction and representation. Furthermore, we extract probabilities from these models and aggregate them using a fuzzy Minkowski Distance approach. This technique serves to minimize error values between observed and ground-truth data, leading to a further enhancement in detection accuracy. For our experiments, we utilize a publicly available dataset containing 6000 endoscopy images. These images are not only enhanced through the application of image contrast enhancement techniques but are also subjected to augmentation processes, effectively enhancing the dataset's diversity and contributing to improved performance in the classification task. After conducting comprehensive assessments, we highlight the strength of our ensemble model compared to single base models and existing methods. The ensemble consistently achieves an impressive accuracy of 99.62%, showcasing its potential as a powerful tool for accurate diagnosis in the field of gastroenterology.

The Curvilinear Impact of Cultural Friction on Foreign Divestment

International business scholars have long recognized the potential influence of cultural differences on foreign divestment; however, the empirical results are mixed. Our study helps resolve this contradiction and contribute to the existing literature in three ways. First, we advocate the use of cultural friction metric, instead of the more traditional cultural distance approach. This overcomes a key limitation in the modelling the impact of cultural differences. The friction construct metric includes an index of firm-specific factors, referred to as the degree of ‘cultural interaction’. This index moderates the impact of cultural distance, reflecting firm—level differences. We also build on calls for more Positive Organizational Scholarship by challenging the negative bias in the international business literature and propose a curvilinear effect of cultural differences on divestment probability. Lastly, we investigate a potential boundary condition—the moderating effect of entry mode on the main hypothesis. Our empirical sample include 2120 Finnish foreign subsidiaries operating in 40 countries during 1970–2010. Our analyses confirm that the cultural differences, when measured by the friction metric, appear to be a significant and superior predictor of subsidiary divestment probability, and that the relationship appears to be U-shaped. Our robustness analyses also highlight the importance of which cultural framework is applied and controlling for selection bias.

Profitability of Pair Trading: A Case of Islamic Indices in Pakistan

The pair trading strategy under portfolio construction is one of the profitable strategies. This is first ever study to observe the pair trading strategy using Islamic Index KSE firms. Distance approach is applied in this study by taking firms under KMI-30 Islamic index by using daily data from year January 1, 2012 to year December 31, 2019. These firms are divided into three subcategories, high-cap, mid-cap and low-cap. After formation of pairs, trading algorithm under various parameterizations is used to observe the profitability. The study concluded with positive and significant returns of top 3, 5, 7 and 10 pairs for each category. In addition, this study also witnessed with positive returns after risk adjustment of market factor for the top 3, 5, 7, and 10 pairs of Islamic index’s firms. These results are accordance with theories of mean revision and market neutrality. The study is contributing to the literature regarding the profitability of pair trading in Islamic Indices firms in Pakistan. Policy Implication for Islamic fund managers and investors is recommended.

A Novel Opportunity Losses-Based Polar Coordinate Distance (OPLO-POCOD) Approach to Multiple Criteria Decision-Making

The ability to make decisions is crucial for achieving success in any field, particularly in areas that involve managing extensive information and knowledge. The process of decision-making in real-world scenarios often involves considering numerous factors and aspects. It can be challenging to make decisions in such complex environments. In this paper, we present a new technique that solves multicriteria decision-making (MCDM) problems by considering opportunity losses-based polar coordinate distance (OPLO-POCOD). MCDM is a subdiscipline of operations research in which some alternatives are evaluated concerning some criteria to choose the most optimal alternative(s). Opportunity loss is a fundamental concept in economics and management, which can be used as a basis for determining the value associated with information. The authors emphasize that the technique incorporates the concept of opportunity losses and uses distance vectors in polar coordinates, making it a compelling approach. By considering opportunity losses, decision-makers gain a better understanding of the trade-offs involved in selecting alternatives, enabling them to make more informed decisions. Finally, the proposed method is exhibited through the use of numerical an example to illustrate its process. Additionally, a comparative sensitivity analysis is conducted to evaluate the outcomes of OPLO-POCOD and compare them with existing MCDM methods. The OPLO-POCOD method is found to have high reliability compared to other methods, as indicated by Spearman’s correlation coefficient, which is greater than 0.9. The method shows a correlation of over 98.5% with TOPSIS, COPRAS, ARAS, and MCRAT methods, demonstrating its robustness and effectiveness. These analyses show the efficiency of the proposed method and highlight the stability of the results.

The use of single-molecule FRET for the characterization of Holliday junctions containing human telomeric DNA: a methodological approach for nanoscale distance, mobility, and stability measurements

The use of single-molecule FRET for the characterization of Holliday junctions containing human telomeric DNA: a methodological approach for nanoscale distance, mobility, and stability measurements

Estimating Scattering Coefficient in a Large and Complex Terrain through Multifactor Association

Complex-terrain clutter presents serious nonuniformity, which has a significant impact on radar target detection, communication, and navigation. The accurate acquisition of the clutter characteristics by measurement or calculation for large and complex terrains has always posed a challenge due to the high costs of the measurement, as well as the intricate and diverse environmental factors. To address this challenge, we proposed a research methodology that leverages the similarity of multidimensional terrain features to infer the clutter characteristics of unmeasured regions, particularly those that are difficult or impossible to measure directly. In order to realize this study object, we constructed a dataset consisting of multidimensional environmental and clutter data to quantitatively characterize the complex environmental information in a vast territory. Within the dataset, we selected two regions with similar terrain characteristics: one region served as the source data for mining and analyzing features, while the other was designated as the target data region for method validation. Through the application of prior-knowledge-based classification and multifactor weight analysis on the dataset, two novel estimation techniques were devised. The first method, designated as PCKRF, blended prior-knowledge classification, weighted K-means clustering, and the random forest (RF) algorithm; and the second method, labeled PCKMW, integrated prior-knowledge classification, weighted K-means clustering, and the minimum weighted distance (MW) approach. In estimating and validating the clutter data from the source region to the target region, the performances of both the PCKRF and PCKMW methods were notably superior to those of the RF, MW, and K-means minimum weighting (KMW). Specifically, the root-mean-squared error (RMSE) was enhanced from a range of 7 dB–10 dB to a range of 4 dB–6 dB, while the determination coefficient (R2) was increased from a range of −1.15–0.09 to a range of 0.25–0.66. The above demonstration illustrates that the current achievements in the clutter estimation methods offer a viable option for accurately recognizing clutter characteristics in complex-terrain environments where comprehensive data collection may be difficult or impossible, with lower human and economic costs.

Population study of long-tailed macaque (Macaca fascicularis) on Deli Island, Banten, Indonesia

Abstract. Fitriana YS, Sulistyadi E, Tohir RK, Hasibuan MM, Rifaie F, Maryanto I, Lubis AM, Rifqi MF. 2024. Population study of long-tailed macaque (Macaca fascicularis) on Deli Island, Banten, Indonesia. Biodiversitas 25: 144-150. Understanding the distribution and population dynamics of primate species is essential for practical conservation efforts. However, such information is often unavailable, even for species whose populations appear abundant. The long-tailed macaque (Macaca fascicularis Raffles, 1821) is a species with a wide range in Southeast Asia, including Sumatra, Java, Kalimantan, Bali, Lombok, and Lesser Sunda in Indonesia. However, comprehensive data on population density across its Indonesian distribution area still needs to be improved. To address this knowledge gap, we conducted a thorough survey and study to estimate the population density of long-tailed macaques on Deli Island, located in Banten Province. Long-tailed macaques were introduced to Deli Island between 1989 and 1990 with a total of 1000 males and 10,000 females. To date, a total of 9,500 individuals have been harvested from this island in the period 1991-2010. We employed the line transect distance sampling (LTDS) method with a perpendicular distance approach that covered all habitat types throughout the island. Our findings revealed that the population density of long-tailed macaques on Deli Island is approximately five individuals per hectare, with an estimated total population of 4,860. The data pertaining to age structure and sex ratio is notably limited, primarily due to challenges in identification caused by dense vegetation. To address these limitations and acquire a more comprehensive understanding, additional research is essential for the accurate determination of the age structure and sex ratio on the island. Our study provides crucial baseline data for conserving this species. We recommend implementing a systematic survey encompassing the entire distribution range of the long-tailed macaque across Indonesia. Such an initiative will be instrumental in assessing its threat status and laying out a comprehensive plan for conservation efforts.

A Fuzzy Inventory Model Using Pentagonal Fuzzy Numbers

Transportation expenses may have an effect on significant business sectors. They could affect a company's price of goods as well as its capacity to remain competitive in the market. Another way to maximize transportation expenses is through inventory management. Prevent needless overstocking and incomplete shipments by keeping an accurate inventory and managing stock levels. This lowers the price of shipping extra items or broken shipments. Nonlinear Programming Kuhn Tucker Method is used to find the optimal solution, which has an impact on the monthly payment. The signed distance approach is used for defuzzification in the proposed model, and the Pentagonal fuzzy number is used to find the lowest cost. To test the inventory model, we create a CSV file and use Weka software to analyse the Economic Order Quantity and total cost.

Quantile Regression in Survival Analysis: Comparing Check-Based Modeling and the Minimum Distance Approach

Introduction: Quantile regression is a valuable alternative for survival data analysis, enabling flexible evaluations of covariate effects on survival outcomes with intuitive interpretations. It offers practical computation and reliability. However, challenges arise when applying quantile regression to censored data, particularly for upper quantiles. The minimum distance approach, utilizing dual-kernel estimation and the inverse cumulative distribution function, shows promise in addressing these challenges, especially with higher-dimensional covariates.
 Methods: This study contrasts two methods within the realm of quantile linear regression for survival analysis: check-based modeling and the minimum distance approach. Effectiveness is assessed across various scenarios through comprehensive simulation.
 Results: The simulation results showed that using the quantile regression model with the minimum distance approach reduces the percentage of root mean square error in parameter estimation compared to the quantile regression models based on the check loss function. Additionally, a larger sample size and reduced censoring percentage led to decreased root mean square error in parameter estimation.
 Conclusion: The research highlights the benefits of using the minimum distance approach for quantile regression. It reduces errors, improves model predictions, captures patterns, and optimizes parameters even with complete data. However, this approach has limitations. The accuracy of estimated quantiles can be influenced by the choice of distance metric and weighting scheme. The assumption of independence between censoring mechanism and survival time may not hold in real-world scenarios. Additionally, dealing with large datasets can be computationally complex.
 

Tourism on the roof of the world: Socio-ecological impacts of tourism on the Indian Trans-Himalaya

Tourism is a rapidly developing industry in ecologically sensitive remote areas, especially in developing countries. Although a lucrative livelihood alternative, it is often responsible for degrading the environment and stratifying the socio-economic setup, if not managed prudently. We assessed the impact of tourism on ecological integrity and local livelihood in wetlands of Changthang Wildlife Sanctuary (CWLS), Indian Trans-Himalaya. Change in behaviour of wetland birds was studied using flight initiation distance (FID) approach to understand the ecological sensitivity of CWLS. We evaluated livelihood contribution of tourism and attitude of local people towards tourism as a futuristic livelihood opportunity using questionnaire-based interviews and focus group discussions. Although traditional communities of CWLS see tourism as a livelihood opportunity; they are still sceptical about its effectiveness and indicated leakage of tourism benefits. People were also dubious about its impact on their socio-cultural and environmental structure. FID estimates have shown level of sensitivity among wetland birds towards presence of tourists. We recommend, site-specific FID based ‘minimum approach distances’ of most sensitive species for delineating buffers around wetlands to ensure no disturbance to waterbirds. Community managed nature-based tourism is suggested to maximise the tourism benefits to local people. Prototypical sustainable tourism activities should be endorsed in the region that can elicit advancement of local economy and efficacy of conservation efforts.

Implementation of K-NN Algorithm to classify the Scholarship Recipients of Aceh Carong at Universitas Malikussaleh

In an effort to increase the efficiency of the scholarship selection process, this research aims to implement the K-Nearest Neighbors (K-NN) algorithm in the classification of scholarship recipients. The research method involves collecting data on scholarship receipts from several previous years based on predetermined criteria such as father's job, mother's job, parent's income, number of parents working, father's last education, and mother's last education. Next, the K-NN algorithm is applied to classify prospective scholarship recipients based on the similarity of their profiles to students who have received previous scholarships. The results of this research indicate that the implementation of the K-NN algorithm in the classification of scholarship admissions at Malikussaleh Aceh Carong University can increase selection accuracy. The experimental results of the accuracy values obtained show that using the K-Nearest Neighbors algorithm with the Euclidean Distance approach and a value of K = 3 produces an algorithm accuracy level of 87.55%. Thus, the K-NN algorithm can be a useful method for scholarship selectors to support more precise and objective decision making.

Influence of shear cut holes on the fatigue performance of hot-rolled 800 MPa automotive steel grades

Experimental fatigue tests were carried out for two t = 3 mm 800 MPa hot-rolled (HR) high-strength steel grades. These steels have good formability, and it is possible to use shear cutting to enable high productivity. The test campaign included unnotched base metal specimens to evaluate the fatigue performance of HR surface and notched specimens with shear-punched and electrical discharge-machined circular holes to evaluate the effect of cutting process and geometrical shape on the fatigue strength capacity. In this context, the properties of the shear-cut affected zone (SAZ) and resulting high tensile residual stresses, and their effects on the fatigue performance, were evaluated. The fatigue test results were first evaluated based on the nominal stress approach, followed by the applications of local approaches. The mean fatigue strength of the specimens with shear cut holes in the nominal stress system was 125 MPa (with a slope parameter of m = 3, stress range at the net cross section), which is at the comparable level to laser cut holes. Fractography analysis, conducted with scanning electron microscopy (SEM), showed local crack-like defects at the SAZ, and the characterization of local defect feature was utilized in the fatigue strength assessment using a multiparametric notch stress method applying effective stresses obtained by the critical distance approach.

Two-stage coordinated optimal dispatching model and benefit allocation strategy for rural new energy microgrid

To aggregate rural biomass energy, distributed power supply, flexibility load, and other resources, a novel structure of the rural Biomass-derived Fuel -based new energy microgrid (BDF-NEM) is proposed. It includes the Biomass Waste Conversion System (BWS), Distributed Renewable Energy (DRE), and Flexible Load Cluster (FLC). The two-stage scheduling optimization framework is designed for the BDF-NEM, minimizing operating costs in the Day-ahead phase and system deviation costs in the Intra-day phase. Uncertainty analysis of wind power plant (WPP) and photovoltaic generators (PV) is conducted using Latin hypercube sampling and the Kantorovich distance approach. To guarantee the equitable allocation of benefits, a benefit allocation mechanism grounded in the enhanced Sharp value approach takes into account operational risks. The analysis of a rural area in northern China demonstrates: (1) After BDF - NEM joined the tiered carbon trading mechanism, although it increased carbon trading costs by 31.27 %, wind power and photovoltaic output increased by 36.08 %, improving new energy consumption; (2) The two-stage scheduling optimization model with daily plan and daily correction can effectively alleviate the deviation caused by wind and solar uncertainty. Although the adjustment cost is 3966.41 yuan, it can avoid the penalty of load loss; (3) The improved benefit allocation based on the Shapley value method reflects the marginal contribution of each system module at the risk level, and when the system modules cooperate with each other, the return is 18.17 % higher than when operating alone, effectively stimulating the enthusiasm to participate in transactions. In summary, the BDF NEM proposed in this article can improve the utilization rate of wind and solar energy, promote the low-carbon development of rural power plants, and achieve local resource allocation in rural areas.

Diabetic retinopathy related homeostatic dysregulation and its association with mortality among diabetes patients: A cohort study from NHANES

AimsTo develop a metric termed the diabetic retinopathy-related homeostatic dysregulation (DRHD) value, and estimate its association with future risk of mortality in individuals with type 2 diabetes. MethodsWith the data of the NHANES, the biomarkers associated with DR were identified from 40 clinical parameters using LASSO regression. Subsequently, the DRHD value was constructed utilizing the Mahalanobis distance approach. In the retrospective cohortof 6420 type 2 diabetes patients, we estimated the associations between DRHD values and mortality related to all-cause, cardiovascular disease (CVD) and diabetes-specific causes using Cox proportional hazards regression models. ResultsA set of 14 biomarkers associated with DR was identified for the construction of DRHD value. During an average of 8 years of follow-up, the multivariable-adjusted HRs and corresponding 95 % CIs for the highest quartiles of DRHD values were 2.04 (1.76, 2.37), 2.32 (1.78, 3.01), and 2.29 (1.72, 3.04) for all-cause, CVD and diabetes-specific mortality, respectively. Furthermore, we developed a web-based calculator for the DRHD value to enhance its accessibility and usability (https://dzwxl-drhd.streamlit.app/). ConclusionsOur study constructed the DRHD value as a measure to assess homeostatic dysregulation among individuals with type 2 diabetes. The DRHD values exhibited potential as a prognostic indicator for retinopathy and for mortality in patients affected by type 2 diabetes.

Distance interventions for enhancing preparedness in informal caregivers of older adults: A systematic review protocol.

Informal caregivers provide care to older adults but report lack of preparedness to enact the role. Intervention programs delivered by distance offer one alternative to support preparedness. Three review studies conducted to date have highlighted the benefits of distance interventions for enhancing preparedness among informal caregivers of older adults. However, these reviews have been limited in presenting and discussing how intervention components influenced outcomes. Additionally, they have not compared different distance delivery approaches for informal caregivers of older adults or assessed their varying impact on preparedness outcomes. These limitations make the effectiveness of diverse distance approaches unclear. To evaluate the effects of distance interventions aimed at enhancing preparedness among informal caregivers of older adults. This protocol follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) guidelines and is guided by the Cochrane Handbook for Systematic Reviews of Interventions. It has been registered in PROSPERO (CRD42023400668). Databases used in the search will include CINAHL, Cochrane Library, EMBASE, MEDLINE, PsycINFO, Scopus, and ProQuest Theses and Dissertations Global. The search will not be restricted by publication year to include all relevant studies. Studies published in English and Portuguese will be included. Study quality will be assessed using Downs and Black's checklist. If metanalysis is possible, it will be performed using the ReviewManager (RevMan) software. The study will be the first of its type to systematically review and synthesize components and approaches of distance interventions aimed at supporting preparedness of informal caregivers of older adults.

Comparison of the Results of the K-Nearest Neighbor (KNN) and Naïve Bayes Methods in the Classification of ISPA Diseases (Case Study: RSUD Fauziah Bireuen)

Acute Respiratory Infection or commonly called (ARI) is a disease caused by bacteria or viruses. (ARI) can attack all ages, especially children. This study aims to compare the accuracy of classification in (ARI) disease. The data used is data from patients affected by (ARI) disease at Fauziah Bireuen Hospital. K-Nearest Neighbors and Naïve Bayes can be used in the classification of (ARI) diseases. Measurement of accuracy using Confusion Matrix in the K-Nearest Neighbors method with the Eulidean Distance approach in the case of (ARI) disease classification obtained a percentage of precision of 91%, recall 84% and accuracy of 88%. While the Naïve Bayes method obtained a percentage of precision of 95%, recall 78% and accuracy of 86%. The results of the accuracy comparison of the two methods show that the K-Nearest Neighbors method has a higher accuracy rate than the Naïve Bayes method.

Implementation of Recurrent Neural Network (RNN) for Question Similarity Identification in Indonesian Language

In a question-and-answer forum, the identification of question similarity is used to determine how similar two questions are. This procedure makes sure that user-submitted questions are compared to the questions in a database for matches to improve system performance on the online Q&A platform. Currently, question similarity is mostly done in foreign languages. The purpose of this research is to identify question similarities and evaluate the effectiveness of the methods used in Indonesian language questions. The data used is a public dataset with labeled pairs of questions as 0 and 1 where label 0 for different pairs of questions and label 1 for the same pairs of questions. The method used is a Recurrent Neural Network (RNN) with the Manhattan Distance approach to calculate the similarity distance between two questions. The question pairs are taken as two inputs with a reference label to identify the similarity distance between the two question inputs. We evaluated the model using three different optimizers namely RMSprop, Adam, and Adagrad. The best results were obtained using the Adam optimizer with 80:20 ratio split-data and overall accuracy is 76%, precision is 74%, recall is 98.8%, and F1-score is 85.1%.

Identification of Hydrochemical Characteristics, Spatial Evolution, and Driving Forces of River Water in Jinjiang Watershed, China

Rivers are an important source of water in humid regions, but their availability is greatly limited by water chemistry. In order to accurately identify the changes in river water chemical composition, the compositional analysis method (CoDA) is proposed from the perspective of compositional data analysis theory, which considers the geochemical riverine system as a whole and detects the compositional changes of the entire watershed. The basic data analysis is carried out by traditional analysis methods, and the results show that the hydrochemical characteristics of different sections of the basin have significant features. The water chemistry of Dongxi River is of the HCO3− Ca type. The water of the Xixi River shows a gradual evolution from the HCO3-Ca type and high SO42− content in the upper reaches to the Cl-Ca type in the lower reaches. The hydrochemistry of river water in the watershed is mainly affected by rock weathering leaching (PC1) and agricultural and domestic pollutant discharge (PC2), with a contribution rate of 48.4% and 19.7%, respectively. Rock weathering, mining, and agricultural pollution are the main factors affecting the chemical composition of river water in different regions. The spatial composition of a single sample at different scales is monitored by the Mahalanobis distance approach in an iterative manner to minimize the influence of a single anomaly on the composition center. The results show that the main reasons for the change in river water chemical composition along the Xixi River are attributed to mine pollution, domestic pollution, and tea plantation and that along the Dongxi River is caused by domestic pollution. The hydrochemical composition changes after the confluence of the Xixi River and Dongxi River are mainly affected by human activities and seawater in urban areas. This research could provide new perspectives and methods for detecting the influences of human and natural factors on the hydrochemistry of river water in humid regions worldwide.

Temporal segmentation of EEG based on functional connectivity network structure

In the study of brain functional connectivity networks, it is assumed that a network is built from a data window in which activity is stationary. However, brain activity is non-stationary over sufficiently large time periods. Addressing the analysis electroencephalograph (EEG) data, we propose a data segmentation method based on functional connectivity network structure. The goal of segmentation is to ensure that within a window of analysis, there is similar network structure. We designed an intuitive and flexible graph distance measure to quantify the difference in network structure between two analysis windows. This measure is modular: a variety of node importance indices can be plugged into it. We use a reference window versus sliding window comparison approach to detect changes, as indicated by outliers in the distribution of graph distance values. Performance of our segmentation method was tested in simulated EEG data and real EEG data from a drone piloting experiment (using correlation or phase-locking value as the functional connectivity strength metric). We compared our method under various node importance measures and against matrix-based dissimilarity metrics that use singular value decomposition on the connectivity matrix. The results show the graph distance approach worked better than matrix-based approaches; graph distance based on partial node centrality was most sensitive to network structural changes, especially when connectivity matrix values change little. The proposed method provides EEG data segmentation tailored for detecting changes in terms of functional connectivity networks. Our study provides a new perspective on EEG segmentation, one that is based on functional connectivity network structure differences.