Euclidean Distance Research Articles

A novel sharding algorithm based on TOPSIS method for blockchain systems

PurposePerformance optimization algorithms based on node attributes are of great importance for sharding blockchain systems. Currently, existing studies on blockchain sharding algorithms consider only random selection sharding strategies. However, the random selection strategy does not perfectly utilize the performance of a node to break the bottleneck of blockchain performance.Design/methodology/approachThis paper proposes a blockchain sharding algorithm called TOPSIS Optimization Sharding System (TOSS), which is based on entropy weight method, relative Euclidean distance and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). It defines a multi-attribute matrix to assess node performance and applies TOPSIS for scoring nodes. Then, an algorithm based on the TOPSIS method is proposed to calculate the performance score of each data node. In addition, an entropy weighting method is introduced to obtain the weights of each attribute to balance the impact of dimensional differences of attributes on the attribute weights. Nodes are ranked by composite scores to guide partitioning.FindingsThe effectiveness of the proposed algorithm in this paper is verified by comparing it with various comparative algorithms. The experimental results show that the TOSS algorithm outperforms the comparison algorithms in terms of performance improvement for the blockchain system, and the throughput metrics are improved by about 20% in comparison.Originality/valueThis study introduces a novel approach to blockchain sharding by incorporating the entropy weight method and relative Euclidean distance TOPSIS into the sharding process. This approach allows for a more effective utilization of node performance attributes, leading to significant improvements in system throughput and overall performance, addressing the limitations of the random selection strategy commonly used in existing algorithms.

Game Pathfinding System Design based on A-Star Pathfinding Algorithm

Abstract. Pathfinding systems are used in many applications today, including navigation systems, autonomous driving systems, and games. The core part of the pathfinding system is the pathfinding algorithm, A Star pathfinding algorithm is a kind of efficient pathfinding algorithm, which is especially suitable for the design of game pathfinding. In this paper, based on the A Star algorithm, Godot game engine and C Sharp language are used to write a pathfinding program. After comparing different pathfinding methods and the estimated function, the A Star algorithm is improved according to the needs of the actual game pathfinding system, so that the pathfinding program can avoid crossing the obstacles through the diagonal by searching the surrounding obstacles and excluding unavailable nodes, and find a shorter path by using the Euclidean distance estimated function. The program applies the improved A Star algorithm in practice, and finally can complete the basic pathfinding needed in the game, which is practical for the design of the game's pathfinding system.

Phenotypic Diversity Analysis in the Sect. Tuberculate (Camellia L.) Population, an Endemic Taxon in China

Sect. Tuberculate Chang belongs to the genus Camellia, which is an endemic group in China and has high research value. However, the phenotypic patterns of this taxon are complex and diverse, and the phenotypic variation in key traits is still unclear. In this study, a total of 212 samples from 18 populations of sect. Tuberculate plants were studied for 30 phenotypic traits of flowers, fruits, and leaves using analysis of variance, correlation analysis, principal component analysis, and cluster analysis. The results showed the following. (1) The plants in sect. Tuberculate were rich in phenotypic trait variation and possessed rich phenotypic diversity. The differentiation of phenotypic traits mainly came from among populations, with leaves (66.804%) being the largest and flowers (53.476%) being the smallest. Qualitative traits (70.264%) were greater than quantitative traits (57.608%). (2) Correlation analyses showed close and complex relationships among the phenotypic traits of flowers, fruits, and leaves. (3) The cumulative contribution of the first 10 principal components was up to 73.49%, which screened out 12 major traits contributing to the phenotypic differences in plants of sect. Tuberculate. (4) Q-type analysis showed that they were classified into 18 taxa at a Euclidean distance of 7.5 and 11 taxa at a Euclidean distance of 10. The 18 populations were not fully clustered according to the geographic distance of the plants, and there was an overlap between some of the populations. In summary, the degree of variation in phenotypic traits among populations of sect. Tuberculate plants is high, which is affected by the climatic environment. The 12 major phenotypic traits screened can be used as the basis for the classification of sect. Tuberculate plants. There are trait overlaps among some populations, which may be affected by the stochastic influence of the geographic climate and gene flow. This study will provide important references for interspecific identification, classification system construction, genetic mechanism, germplasm resource conservation, and exploitation of plants in sect. Tuberculate.

Score-based likelihood ratios for barefootprint evidence using deep learning features.

As the court put forward higher requirements for quantitative evaluation and scientific standards of forensic evidence, how to objectively and scientifically express identification opinions has become a challenge for traditional forensic identification methods. Score-based likelihood ratios are mathematical methods for quantitative evaluation of forensic evidence. However, due to the subtle differences in inter-class barefootprints, there is no automatic barefootprints matching algorithm with high accuracy under large-scale dataset validation, and there are few studies related to deep learning barefootprint features for evidence evaluation in court. Therefore, score-based likelihood ratios for barefootprint evidence using deep learning features are proposed by this paper. Firstly, the largest barefootprint dataset (BFD) is constructed, which contains 54,118 barefootprint images from 3000 individuals. Then, an automatic barefootprint feature extraction and matching algorithm is proposed, which achieves a retrieval accuracy of 98.4% on BFD and an AUC of 0.989 for barefootprint validation. Next, Cosine distance, Euclidean distance and Manhattan distance are employed to measure the comparison scores between intra-class and inter-class barefootprints using deep learning features in four dimensions of 64, 128, 512 and 1024, respectively. The performance of proposed model is evaluated by comparing the values and the Tippett plot. Finally, simulated crime scene barefootprint samples are constructed to verify the practical application of the proposed method, which provide further support for the quantitative evaluation of barefootprint evidence in court.

An intelligent spectral identification approach for the simultaneous detection of endocrine-disrupting chemicals in aquatic environments.

With the rapid progression of industrialization, the application and release of endocrine disruptors (EDCs), including bisphenol A (BPA), octylphenol and nonylphenol have significantly increased, presenting substantial health hazards. Conventional analytical techniques, such as high-performance liquid chromatography and gas chromatography-mass spectrometry, are highly sophisticated but suffer from complex procedures and high costs. To overcome these limitations, this study introduces an innovative spectral methodology for the simultaneous detection of multiple aquatic multicomponent EDCs. By leveraging chemical machine vision, specifically with convolutional neural network (CNN) models, we employed a long-path holographic spectrometer for rapid, cost-effective identification of BPA, 4-tert-octylphenol, and 4-nonylphenol in aqueous samples. The CNN, refined with the ResNet-50 architecture, demonstrated superior predictive performance, achieving detection limits as low as 3.34, 3.71 and 4.36 μg/L, respectively. The sensitivity and quantification capability of our approach were confirmed through the analysis of spectral image Euclidean distances, while its universality and resistance properties were validated by assessments of environmental samples. This technology offers significantly advantages over conventional techniques in terms of efficiency and cost, offering a novel solution for EDC monitoring in aquatic environments. The implications of this research extend beyond improved detection speed and cost reduction, presenting new methodologies for analyzing complex chemical systems and contributing to environmental protection and public health.

Simplifying traffic simulation - from Euclidean distances to agent-based models

AbstractUrban settings require a thorough understanding of traffic patterns to best manage traffic, be prepared for emergency scenarios and to guide future infrastructure investments. In addition to analyzing collected traffic data, traffic modeling is an important tool that often requires detailed simulations that can be computationally intensive and time-consuming. A well-known comprehensive simulation framework is MATSim. On the other hand, simpler shortest-path routing systems that compute trips on an individual basis promise faster computations. The primary focus of this study is to assess the viability of a fast shortest path routing system as a method of traffic simulation. This study compares the MATSim with the Graphhopper routing system. Key metrics include travel time accuracy, congestion levels, route similarity, vehicle miles traveled, and average travel time. By analyzing these metrics, this study shows that a shortest-path routing system can serve as an effective and expedient approximation of more resource intensive simulation frameworks. This has significant implications for authorities and planners, as it offers a quick and efficient tool for traffic management and decision-making during critical events, enhancing their ability to respond quickly and effectively to dynamic traffic conditions.

Average Case Subquadratic Exact and Heuristic Procedures for the Traveling Salesman 2-OPT Neighborhood

We describe an exact algorithm for finding the best 2-OPT move that, experimentally, was observed to be much faster than the standard quadratic approach for a large part of a best-improvement local search convergence starting at a random tour. To analyze its average-case complexity, we introduce a family of heuristic procedures and discuss their complexity when applied to a random tour in graphs whose edge costs are either uniform random numbers in [0, 1] or Euclidean distances between random points in the plane. We prove that, for any probability p, there is a heuristic in the family that can find the best 2-OPT move with probability at least p in average-time [Formula: see text]) for uniform instances and [Formula: see text] for Euclidean instances. The exact algorithm is then proved to be even faster in the sense that in those instances in which a heuristic finds the best move, the exact algorithm finds it in a smaller time. We give empirical evidence that a slight variant of our algorithm finds the best move in [Formula: see text] time on both types of instances, achieving the best possible performance for this particular problem. Computational experiments are reported to show the effectiveness of our algorithms, both in best-improvement and in first-improvement 2-OPT local search. History: Accepted by Andrea Lodi, Area Editor for Design and Analysis of Algorithms: Discrete. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0169 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0169 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Predicting habitat functionality using habitat network models in urban planning

Recently, the European Union adopted a Nature restoration law that aims to stop the ongoing decline of biodiversity and even bring back nature to cities. This is challenging as urbanization is an ongoing process that thrives for more land and densification. In this paper we describe a new Open-Source GIS-tool that automates habitat network analyses and simultaneously generates several maps that can be used for assessments, targeting species survival in urban environments. One result of particular interest is the habitat functionality map that combines values of habitat quality and connectivity. We tested the tool’s ability to predict habitat functionality using amphibian occurrence data observed from the city of Gothenburg in Sweden. Our evaluation shows that habitat functionality was generally a good predictor of amphibian distribution. However, the predictability was sensitive to the cartographic representativity of the input biotope map used. Also, predictions of habitat functionality improved when estimating dispersal probabilities using the Cost-distance algorithm, compared to when using Euclidean distance from reproductive habitats. This finding supports the need to use connectivity models that are responsive to variation and changes in roads, traffic volumes and buildings when performing effect analyses on biodiversity in cities. Finally, we demonstrate how the tool can be used to easily identify areas where restoration measures can effectively increase habitat functionality for target species. This can help planners to find efficient solutions for increasing biodiversity within urban areas.

CNSC-54. CENTRAL AND BOUNDARY-DRIVEN GROWTH PATTERNS DOMINATE RESPECTIVELY IDH WILD-TYPE AND MUTANT GLIOMAS

Abstract Diffuse gliomas are the most prevalent primary malignant brain tumors in adults and are characterized by invasive growth and poor prognosis. Tumor cells infiltrate the brain parenchyma and spread to distant sites. The aim of this study is to characterize the spatial heterogeneity, mode, and direction of tumor development to identify key areas for localized treatment. Neuronavigation was employed to collect n=134 image-guided samples from n=16 adult patients with diffuse gliomas, including n=7 IDH wild-type (IDHwt) and n=9 IDH mutant (IDHmut) tumors from regions with and without MRI abnormalities. Collected samples were subjected to targeted and shallow whole-genome sequencing. Somatic variants were used to determine the evolutionary processes governing heterogeneity. Examination of the dN/dS ratio, Tajima’s D score, and MOBSTER model suggested that all tumors are consistent with neutral evolution, as previously reported. Considering that genetic heterogeneity was a result of stochastic mutations, we used phylogeographic relationships between samples to dissect the spatiotemporal development of these tumors. Evolutionary (patristic) and cartesian (Euclidean) distances between sample pairs from the same patient were correlated in n=2/9 IDHmut and n=2/7 IDHwt patients, suggesting that migratory or punctuated evolutionary processes play a major role in tumor diffusion. An examination of spatial diffusion revealed that the latest descendant appeared peripherally in n=5/9 IDHmut patients, suggesting boundary-driven growth. In the remaining cases, three patients exhibited centrally-driven growth, whereas a fourth patient displayed a mixed pattern. In contrast, in n=4/7 IDHwt patients, descendants appeared closer to the tumor center, suggesting centrally-driven growth. In the remaining three IDHwt patients, one patient showed a diffusion that was consistent with boundary-driven growth, while the other two patients showed a mixed pattern. Taken together our data suggest that IDHwt tumors tend to develop centrally, whereas IDHmut tumors preferably develop outward. Our data could aid in tailoring localized treatment based on projected growth patterns.

Method of bed exit intention based on the internal pressure features in array air spring mattress

With the population ages, many patients are unable to receive comprehensive care, leading to an increase in hazardous incidents, particularly falls occurring after getting out of bed. To address this issue, this paper proposes a method for recognizing bed-exit intentions using an array air spring mattress. The method integrates convolutional neural networks with feature point matching techniques to identify both global and local features of the array air spring. For global features, a one-dimensional focal loss convolutional neural network (1D-FLCNN) model is employed to classify eight internal pressure time series and determine bed-exit status based on global features. For local features, the distribution matrix and feature point matrix of the internal pressure features are extracted to represent the spatial distribution of bed-exit postures. Euclidean distance is utilized to measure the similarity between these matrices and match bed-exit postures. Finally, the recognition results from both feature types are combined using a logical OR operation to produce the final result. Experimental validation confirms that the proposed method greatly improves the anti-interference capability and effectively avoids the problem of non-recognition due to body position and external environment.

Low-carbon economic optimization strategy for industrial loads in parks considering source-load-price multivariate uncertainty

A low-carbon economic optimization method is proposed for industrial loads in parks considering multivariate uncertainty. Model of dynamic load node carbon intensity is proposed considering the uncertainty of clean energy and dynamic conventional units carbon intensity based on system currents. The robustness is improved based on multi-interval uncertainty set. The gap is filled by hybrid copula model with Generalized Autoregressive Conditional Heteroskedasticity (GARCH) prices in the uncertainty model. The GARCH is used to built the marginal distribution function of prices, and a hybrid copula model is proposed to obtain the joint probability density function based on the weights calculated by Euclidean distance. The LNCI probability distributions of the regulation potential of loads are proposed with the virtual energy storage and the virtual power model to improve the descriptive ability of uncertainty based on the expansion and contraction functions. Finally, the typical scenarios are extracted based on Monte Carlo and Wasserstein measures. The Column sum Constraint Generation algorithm and strong duality theory are used to get the robust-stochastic optimization. The method proposed in the paper has a positive effect on enterprises to rationalize their production schedules, reduce electricity and carbon emissions costs and increase the revenue from participating in grid regulation.

Concurrent normals problem for convex polytopes and Euclidean distance degree

Concurrent normals problem for convex polytopes and Euclidean distance degree

Fault Detection Strategy of Partial Least Squares Based on Temporal Neighborhood Difference

ABSTRACTAiming at the difficulty of detecting time‐lag faults in dynamic processes, a fault detection strategy based on time neighborhood difference (TND) is proposed, and it is introduced into the partial least squares (PLS) method to suggest the PLS‐TND fault detection method. The TND method takes the mean to the multibatch training set to obtain a baseline training set, and it constructs the mean squared Euclidean distance (MSED) statistic by calculating the average distance between the sample's first k‐moments neighborhood samples and samples at the same moment in the baseline training set. The TND method can help the PLS method to effectively detect time‐lag faults and significantly improve the fault detection capability of PLS by measuring the overall positional difference between the temporal neighborhood sample set of the sample and its temporal neighborhood sample set in the baseline training set. The PLS‐TND method is compared with some classical fault detection methods through a numerical simulation process and a Continuous Stirred Tank Reactor (CSTR) system design fault detection experiment. The PLS‐TND method gives the best performance of fault detection.

Machine learning and analytic hierarchy process integration for selecting a sustainable tractor.

Selecting the appropriate tractor for small-scale farms is a complex process due to the multitude of technical, environmental, and economic criteria that must be evaluated. This study addresses this challenge by integrating the Analytic Hierarchy Process (AHP) with machine learning (ML) to reduce the number of criteria and simplify the decision-making process. The research aims to determine the most relevant criteria aligned with sustainable development goals for selecting the right tractor, focusing on small farms in the Egyptian Delta. Four tractors, with horsepower ranging from 55 to 95, were evaluated based on inputs from forty-two governmental service providers in the study area. Initially, nine criteria were identified, encompassing key technical, environmental, and economic factors. These criteria were reduced to three-price, power, and maintenance costs by weights 0.142, 0.334, and 0.525, respectively-using Hierarchical Agglomerative Clustering with Euclidean distance. This reduction streamlined the selection process, making it more practical for farmers. Results show that the second tractor (T2), with a priority score of 0.326 and a normalized value of 33.4%, emerged as the optimal choice for small-scale farmers, outperforming the first tractor (T1) (28.7%) and third tractor (T3) (21%). Integrating AHP and ML simplifies tractor selection, ensuring sustainability, cost-efficiency, and operational effectiveness.

A Comparison of the Impacts of Different Drying Methods on the Volatile Organic Compounds in Ginseng

Ginseng (Panax ginseng C. A. Meyer) is a valuable plant resource which has been used for centuries as both food and traditional Chinese medicine. It is popular in health research and markets globally. Fresh ginseng has a high moisture content and is prone to mold and rot, reducing its nutritional value without proper preservation. Drying treatments are effective for maintaining the beneficial properties of ginseng post-harvest. In this study, we investigated the effects of natural air drying (ND), hot-air drying (HAD), vacuum drying (VD), microwave vacuum drying (MVD), and vacuum freeze drying (VFD) on volatile organic compounds (VOCs) in ginseng. The results showed that the MVD time was the shortest, followed by the VFD, VD, and HAD times, whereas the ND time was the longest, but the VFD is the most beneficial to the appearance and color retention of ginseng. A total of 72 VOCs were obtained and 68 VOCs were identified using the five drying methods based on gas chromatography–ion mobility spectrometry (GC-IMS) technology, including 23 aldehydes, 19 alkenes, 10 alcohols, 10 ketones, 4 esters, 1 furan, and 1 pyrazine, and the ND method was the best for retaining VOCs. GC-IMS fingerprints, principal component analysis (PCA), Euclidean distance analysis, partial least squares discriminant analysis (PLS-DA), and cluster analysis (CA) can distinguish ginseng from different drying methods. A total of 29 VOCs can be used as the main characteristic markers of different drying methods in ginseng. Overall, our findings provide scientific theoretical guidance for optimizing ginseng’s drying methods, aromatic health effects, and flavor quality research.

An effective Key Frame Extraction technique based on Feature Fusion and Fuzzy-C means clustering with Artificial Hummingbird

Key frame extraction is very important in video summarization and content-based video analysis to address the problem of data redundancy in a video. Key frame extraction enables quick navigation and expert video arrangement in many applications. The visually impaired can benefit from the use of key frame extraction for rapid object recognition and tracking. Most key frame extraction techniques consider only a single visual feature instead of multiple features or full pictorial information of the video. This study proposes a key frame extraction method from a video that (i) first removes insignificant frames by pre-processing, (ii) second, four visual and structural feature differences among the consecutive frames are extracted and aggregated to identify informative frames, (iii) third, to cluster the obtained frames, a hybrid FCM-AHA method is proposed by combining Fuzzy C-means(FCM) with artificial hummingbird optimization algorithm (AHA) to circumvent the local minima trapping problem of FCM, and finally, from each cluster, the two frames having greatest Euclidean distance from all the other frames within a cluster is selected as key frames to remove redundant frames. Experimental results on the Open video and YouTube datasets show that the suggested method outperforms state-of-the-art methods both in terms of subjective qualitative analysis and objective quantitative evaluation, e.g., Precision, Recall, and F-score. Further, results are also taken on real video to demonstrate its applicability in real-life applications.

Online Euclidean Spanners

In this article, we study the online Euclidean spanners problem for points in \(\mathbb{R}^{d}\) . Given a set \(S\) of \(n\) points in \(\mathbb{R}^{d}\) , a \(t\) -spanner on \(S\) is a subgraph of the underlying complete graph \(G=(S,\binom{S}{2})\) , that preserves the pairwise Euclidean distances between points in \(S\) to within a factor of \(t\) , that is the stretch factor . Suppose we are given a sequence of \(n\) points \((s_{1},s_{2},\ldots,s_{n})\) in \(\mathbb{R}^{d}\) , where point \(s_{i}\) is presented in step \(i\) for \(i=1,\ldots,n\) . The objective of an online algorithm is to maintain a geometric \(t\) -spanner on \(S_{i}=\{s_{1},\ldots,s_{i}\}\) for each step \(i\) . The algorithm is allowed to add new edges to the spanner when a new point is presented but cannot remove any edge from the spanner. The performance of an online algorithm is measured by its competitive ratio, which is the supremum, over all sequences of points, of the ratio between the weight of the spanner constructed by the algorithm and the weight of an optimum spanner. Here, the weight of a spanner is the sum of all edge weights. First, we establish a lower bound of \(\Omega(\varepsilon^{-1}\log n/\log\varepsilon^{-1})\) for the competitive ratio of any online \((1+\varepsilon)\) -spanner algorithm, for a sequence of \(n\) points in 1-dimension. We show that this bound is tight, and there is an online algorithm that can maintain a \((1+\varepsilon)\) -spanner with competitive ratio \(O(\varepsilon^{-1}\log n/\log\varepsilon^{-1})\) . Next, we design online algorithms for sequences of points in \(\mathbb{R}^{d}\) , for any constant \(d\geq 2\) , under the \(L_{2}\) norm. We show that previously known incremental algorithms achieve a competitive ratio \(O(\varepsilon^{-(d+1)}\log n)\) . However, if the algorithm is allowed to use additional points (Steiner points), then it is possible to substantially improve the competitive ratio in terms of \(\varepsilon\) . We describe an online Steiner \((1+\varepsilon)\) -spanner algorithm with competitive ratio \(O(\varepsilon^{(1-d)/2}\log n)\) . As a counterpart, we show that the dependence on \(n\) cannot be eliminated in dimensions \(d\geq 2\) . In particular, we prove that any online spanner algorithm for a sequence of \(n\) points in \(\mathbb{R}^{d}\) under the \(L_{2}\) norm has competitive ratio \(\Omega(f(n))\) , where \(\lim_{n\rightarrow\infty}f(n)=\infty\) . Finally, we provide improved lower bounds under the \(L_{1}\) norm: \(\Omega(\varepsilon^{-2}/\log\varepsilon^{-1})\) in the plane and \(\Omega(\varepsilon^{-d})\) in \(\mathbb{R}^{d}\) for \(d\geq 3\) .

High frequency radar error classification and prediction based on K-means methods

This study aims to characterize the high frequency radar and numerically simulated low-frequency filtered currents in the south-eastern Bay of Biscay (study area) using a K-means classification algorithm based on an improved Euclidean Distance calculation method that does not take missing values. The errors between observations and simulations was estimated and predicted based on this classification method. Results indicate that predominantly eastward (northward) currents over the Spanish (French) continental shelf/slope in winter and more variable currents in the west and south-west in summer. The model classification results for circulation characteristics are in relatively good agreement with HF radar results, especially for currents on the Spanish (French) shelf/slope. In addition, the probabilistic relationship between observed and modeled currents was explored, obtaining the probability of occurrence of modeled current groups when each group of observed currents occurs. Finally, predictions of model and observed current errors were made based on the classification results, and it was found that the predictions based on the classification of all data had the smallest errors, with a 17% improvement over the unclassified control experiment. This study provides a foundation for subsequent model error testing, forecast product improvement and data assimilation.

"Guangzhou-Shenzhen" Urban Linkage and Cooperation Study

As important cities in the Guangdong-Hong Kong-Macao Greater Bay Area, the development of Shenzhen and Guangzhou is of great significance for achieving resource complementarity and promoting industrial synergy in the region. Based on data from 2002 to 2022, this paper uses the Euclidean distance measurement algorithm to calculate the gravitational strength between the two cities, and assesses their competitive and complementary relationships through the geographical affinity strength. Combining these relationships, this paper analyzes the directions in which the two cities can achieve complementary development in the future three industries, and proposes suggestions for optimizing their industrial structures and improving government policies.

A numerical conductive network model for investigating the strain-sensing response of graphene nanoplatelets-filled elastomeric strain sensors

Strain sensors have attracted great attentions for practical applications such as human-machine interface. It is challenging to model the electrical behavior of elastomeric strain sensors modified with nanomaterials. This study presents a finite element simulation to explore the piezoresistivity of graphene nanoplatelet (GNP)-filled elastomeric nanocomposites. The percolation network model is employed to determine the critical distance necessary for electrical percolation. The finite element percolation network model uses the Euclidian distance between GNPs distributed inside the representative volume element to calculate the resistivity. The influence of various parameters, including GNP alignment direction, aspect ratio, and volume fraction, on the resistivity changes of the nanocomposite is investigated. Results demonstrate that nearly 20% more tunneling distance is allowed for percolation of nanocomposite with 30% larger aspect ratio GNPs. Findings reveal that as the volume fraction of GNPs increases, the critical distance for the electrical percolation decreases significantly.