Euclidean Approach Research Articles

Approaches to modeling interpersonal complementarity in intensive longitudinal data

Contemporary integrative interpersonal theory (CIIT) posits that successful social interactions are characterized by complementarity: correspondence in interpersonal warmth and reciprocity in interpersonal dominance. Interactions with high complementarity evoke more positive affect and less negative affect. Modeling complementarity is challenging because it requires capturing the interpersonal behavior of individuals along the two dimensions of warmth and dominance. This study compares three approaches—statistical interaction, multilevel response surface analysis, and Euclidean distance—for modeling complementarity across four datasets. The approaches varied in the consistency of findings and proportion of variance explained. Findings suggest the Euclidean approach for parsimony and theoretical coherence, whereas multilevel response surface analysis is preferable for comprehensively modeling the interplay of self and other on the interpersonal dimensions.

Quantum transitions, detailed balance, black holes, and nothingness

We consider vacuum transitions by bubble nucleation among 4D vacua with different values and signs of the cosmological constant Λ, including both up and down tunnelings. Following the Hamiltonian formalism, we explicitly compute the transition probabilities for all possible combinations of initial and final values of Λ and find that up tunneling is allowed starting not only from dS spacetime but also from AdS and Minkowski spacetimes. We trace the difference with the Euclidean approach, where these transitions are found to be forbidden, to the difference of treating the latter spacetimes as pure (vacuum) states rather than mixed states with correspondingly vanishing or infinite entropy. We point out that these transitions are best understood as limits of the corresponding transitions with black holes in the zero mass limit M→0. We find that detailed balance is satisfied provided we use the Hartle-Hawking sign of the wave function for nucleating spacetimes. In the formal limit Λ→−∞, the transition rates for anti–de Sitter (AdS) to dS agree with both the Hartle-Hawking and Vilenkin amplitudes for the creation of dS from nothing. This is consistent with a proposal of Brown and Dahlen to define “nothing” as AdS in this limit. For M≠0, the detailed balance is satisfied only in a range of mass values. We compute the bubble trajectory after nucleation and find that, contrary to the M=0 case, the trajectory does not correspond to the open universe slicing of dS. We briefly discuss the relevance of our results to the string landscape. Published by the American Physical Society 2024

Time of concentration estimated of overland flow

Time of concentration (Tc) is a crucial aspect in the hydrological model, especially for determining flood discharge. Time concentration is the amount of time required for water to go from a watershed’s farthest point to its outflow. Although it is an important value, however, Tc does not have a universal equation that can be used as a reference. Based on the literature review, there are several concentration time calculation methods. Each method has its parameters and approach to produce varied values. Several experimental methods were applied to calculate concentration time at sites, among others are Kirpich, SCS Lag, and FAA. The calculation results are displayed in a dendrogram with group division by using the Euclidean approach. The calculation results show that the difference in Tc values can reach up to 500%, hence Grimaldi calls it a paradox in modern hydrology.

Real-time dynamics of false vacuum decay

We investigate false vacuum decay of a relativistic scalar field initialized in the metastable minimum of an asymmetric double-well potential. The transition to the true ground state is a well-defined initial-value problem in real time, which can be formulated in nonequilibrium quantum field theory on a closed time path. We employ the nonperturbative framework of the two-particle irreducible (2PI) quantum effective action at next-to-leading order in a large-N expansion. We also compare to classical-statistical field theory simulations on a lattice in the high-temperature regime. By this, we demonstrate that the real-time decay rates are comparable to those obtained from the conventional Euclidean (bounce) approach. In general, we find that the decay rates are time dependent. For a more comprehensive description of the dynamics, we extract a time-dependent effective potential, which becomes convex during the nonequilibrium transition process. By solving the quantum evolution equations for the one- and two-point correlation functions for vacuum initial conditions, we demonstrate that quantum corrections can lead to transitions that are not captured by classical-statistical approximations. Published by the American Physical Society 2024

A fuzzy Euclidean taxicab distance-based MCDM approach for optimal personnel selection

A fuzzy Euclidean taxicab distance-based MCDM approach for optimal personnel selection

Comparing densely calculated facial soft tissue depths for craniofacial reconstruction: Euclidean vs. perpendicular distances

ABSTRACT For surgical craniofacial reconstruction, preoperative planning may be limited by missing 3D skeletal geometry. In forensic sciences, ‘reconstruction’ models the 3D facial structure from skull geometries using soft-tissue depth mapping. This work investigates ‘reverse engineering’ the forensics’ TD Morpheus model to infer the bony shape from 3D facial surfaces by subtracting tissue depths inwards along the normal vectors. This approach using Euclidean tissue depths successfully estimated the upper and outermost skeletal regions (i.e. frontal, zygoma, and nasal bones) in 24 head CT scans, but concave skeletal surfaces were inaccurately evaluated where the face is convex yielding misshapen anatomy around the orbits and zygomatic arches. A perpendicular tissue depth algorithm was developed to probe inwards along the face’s normal vectors until contacting bone, demonstrating superior performance to the Euclidean depth approach. Accurate regional tissue depths achievable with this approach may provide a useful bridge to connect the 3D face and underlying skull geometry, with the potential for application in craniofacial reconstruction.

English

Breast cancer is among the top causes of fatalities related to cancer in females. Radiologists commonly use mammogram images to detect breast tumors in their early stages. However, mammography can produce low-contrast images, making it difficult and time-consuming to segment abnormal regions. Deep convolutional neural networks (CNNs) are commonly used for image evaluations. This study used deep CNN models to develop a computer-aided diagnostic (CAD) system for feature extraction and classification. The proposed approach consists of three phases. In the first phase, a shallow, deep CNN model comprising five convolutional layers, five max-pooling layers, one batch normalization layer, and one dropout layer was developed and used to extract recombined images and novel features. In the second phase, the Inception-v3 model was used for label smoothing and classification due to its multiple filters with different sizes. In the third phase, features were extracted using shallow, deep CNN and Inception-v3 models. The Infallible Euclidean distance-based nonlinear dimensionality reduction approach was used to minimize dimensionality. Finally, the Gini-index-based C4.5 decision tree was used for the binary classification of mammogram images from the Digital Database for Screening Mammography (DDSM) + Curated Breast Imaging Subset of DDSM (CBIS-DDSM) and Mammographic Image Analysis Society (MIAS) datasets. The proposed hybrid shallow, deep CNN and Inception-v3 model achieved 99.52% accuracy, a 96% AUC on the DDSM + CBISDDSM dataset, and an accuracy of 97.53% and an AUC value of 97% on the MIAS dataset. Compared with other cutting-edge CAD systems, the proposed hybrid approach achieved higher accuracy by combining in-depth features across both datasets.

Breast Cancer Detection using Decision Tree and K-Nearest Neighbour Classifiers

Data mining has the most important role in healthcare for discovering hidden relationships in big datasets, especially in breast cancer diagnostics, which is the most popular cause of death in the world. In this paper two algorithms are applied that are decision tree and K-Nearest Neighbour for diagnosing Breast Cancer Grad in order to reduce its risk on patients. In decision tree with feature selection, the Gini index gives an accuracy of %87.83, while with entropy, the feature selection gives an accuracy of %86.77. In both cases, Age appeared as the most effective parameter, particularly when Age<49.5. Whereas Ki67 appeared as a second effective parameter. Furthermore, K- Nearest Neighbor is based on the minimum error rate, and the test maximum accuracy for K_value selection with an accuracy of 86.24%. Where the distance metric has been assigned using the Euclidean approach. From previous models, it seems that Breast Cancer Grade2 is the most prevalent type. For the future perspective, a comparative study could be performed to compare the supervised and unsupervised data mining algorithms.

Improved Performance on Wireless Sensors Network Using Multi-Channel Clustering Hierarchy

Wireless sensor network is a network consisting of many sensor nodes that function to scan certain phenomena around it. WSN has quite a large problem in the form of delay and data loss which results in low WSN performance. This study aims to improve WSN performance by developing a cluster-based routing protocol. The cluster formation is carried out in several stages. The first is the formation of the cluster head which is the channel reference to be used by node members by means of probability calculations. The second determines the closest node using the Euclidean approach when looking for the closest member of the node to the cluster head. The third is determination of the node members by means of single linkage grouping by looking for proximity to CH. The performance of the proposed MCCH method is then tested and evaluated using QoS parameters. The results of this research evaluation use QoS parameters for testing the MCCH method, channel 1 throughput 508.165, channel 2 throughput 255.5661, channel 3 throughput 479.8289, channel 4 throughput 646.5618.

Thermodynamics of Taub-NUT and Plebanski solutions

We observe the parallel between the null Killing vector on the horizon and degenerate Killing vectors at both north and south poles in Kerr-Taub-NUT and general Plebanski solutions. This suggests a correspondence between the pairs of the angular momentum/velocity and the NUT charge/potential. We treat the time as a real line such that the Misner strings are physical. We find that the NUT charge spreads along the Misner strings, analogous to that the mass in the Schwarzschild black hole sits at its spacetime singularity. We develop procedures to calculate all the thermodynamic quantities and we find that the results are consistent with the first law (Wald formalism), the Euclidean action and the Smarr relation. We also apply the Wald formalism, the Euclidean action approach, and the (generalized) Komar integration to the electric and magnetic black holes in a class of EMD theories, and also to boosted black strings and Kaluza-Klein monopoles in five dimensions, to gain better understandings of how to deal with the subtleties associated with Dirac and Misner strings.

Enabling effective location-based services for road networks using spatial mining

A co-location pattern represents a subset of Boolean spatial attributes whose instances are located in a close geographic space. These patterns are important for location-based services. There are many methods for co-location pattern mining where the distance between the events in close geographic proximity is calculated using a straight-line distance called Euclidean distance. Since most of the real-time tasks are bounded to the road networks, the results computed using Euclidean distance is not appropriate. So to compute co-location patterns involving network we define a model where initially a network model is defined and the neighbourhood is obtained by using network distance. By comparing this approach with the previous Euclidean approach, the results obtained for co-location patterns on a road network are accurate. Our experimental results for synthetic and real data show that the proposed approach is efficient and accurate for identifying co-location patterns involving network entities.

A Novel Model for Evaluating the Operation Performance Status of Rolling Bearings Based on Hierarchical Maximum Entropy Bayesian Method

Information such as probability distribution, performance degradation trajectory, and performance reliability function varies with the service status of rolling bearings, which is difficult to analyze and evaluate using traditional reliability theory. Adding equipment operation status to evaluate the bearing operation performance status has become the focus of current research to ensure the effective maintenance of the system, reduce faults, and improve quality under the condition of traditional probability statistics. So, a mathematical model is established by proposing the hierarchical maximum entropy Bayesian method (HMEBM), which is used to evaluate the operation performance status of rolling bearings. When calculating the posterior probability density function (PPDF), the similarities between time series regarded as a weighting coefficient are calculated using overlapping area method, membership degree method, Hamming approach degree method, Euclidean approach degree method, and cardinal approach degree method. The experiment investigation shows that the variation degree of the optimal vibration performance status can be calculated more accurately for each time series relative to the intrinsic series.

Vacuum decay in the Lorentzian path integral

We apply the Lorentzian path integral to the decay of a false vacuum and estimate the false-vacuum decay rate. To make the Lorentzian path integral convergent, the deformation of an integration contour is performed by following the Picard-Lefschetz theory. We show that the nucleation rate of a critical bubble, for which the corresponding bounce action is extremized, has the same exponent as the Euclidean approach. We also extend our computation to the nucleation of a bubble larger or smaller than the critical one to which the Euclidean formalism is not applicable.

SPILLOVER EDUCATED OF INDONESIAN MIGRANT WORKERS (IMW) ON CENTRAL JAVA'S ECONOMIC GROWTH

Indonesian Migrant Workers (TKI) or now known as Indonesian Migrant Workers (IMW) contribute to the economic growth of Central Java Province through foreign exchange earnings, and increasing work productivity. The purpose of this study is to analyze the spatial interaction patterns of IMW locally and globally. This research method uses a euclidean distance weight matrix approach to calculate the Local Moran Index and the Global Moran Index. The novelty of this research is to consider Indonesian workers or IMW with higher education. The conclusion of this study is that the results of the Local Moran Index empirical research identify that there are similar or dissimilar patterns of spatial interaction (within in group) in Cilacap Regency, Brebes Regency, Kendal Regency. However, in 2021 Indonesian Migrant Workers (IMW) from Central Java identified a pattern of spatial interaction (within in group), both similar and dissimilar, in the positions of Cilacap Regency and Kendal Regency. Changes in the Local Moran's Index of Indonesian Migrant Workers (IMW) in 2010 and 2021 were allegedly due to the placement of the number of Indonesian Migrant Workers (IMW) to the destination country which was limited in 2021. Initially, Indonesian Migrant Workers (IMW) in 2010 were Indonesian Migrant Workers (IMW) its placements are in 27 countries, decreasing to 17 countries in 2021. The results of empirical research on the negative Global Moran's I coefficient on Indonesian workers (IMW) with higher education from 2010 to 2021 identify that the spatial interaction pattern of Indonesian workers (IMW) with higher education in Central Java Province is divergent or spreads from districts/cities to district/city.

Euclidean time approach to entanglement entropy on lattices and fuzzy spaces

In a recent letter, Phys. Lett. B792 (2019) 60, we developed a novel Euclidean time approach to compute Rényi entanglement entropy on lattices and fuzzy spaces based on Green’s function. The present work is devoted in part to the explicit proof of the Green’s matrix function formula which was quoted and used in the previous letter, and on the other part to some applications of this formalism. We focus on scalar theory on 1+1 lattice. We also use the developed approach to systematically go beyond the Gaussian case by considering interacting models, in particular our results confirm earlier expectations concerning the correction to the entanglement at first order. We finally outline how this approach can be used to compute the entanglement entropy on fuzzy spaces for free and interacting scalar theories.

Autocorrelation testing on residual spatial logistic regression model with Euclidean distance matrix approach

The existence of spatial effects should not be ignored because it will reduce the goodness of the model. One type of regression analysis that is quite widely used is logistic regression analysis. Spatial logistic regression modelling incorporates spatial effects into the logistic regression model with the expectation that the residuals generated from the model are independent or there is no autocorrelation. The purpose of this study was to obtain the results of spatial autocorrelation testing using a spatial logistic regression model with a Euclidean matrix approach. The results of the study were applied to natural disaster mitigation data in Kupang Regency, Nusa Tenggara Timur Province in 2020, where the distribution of areas in Kupang Regency by village/urban village has spatial autocorrelation. Spatial autocorrelation testing was carried out with Moran's I test to determine the presence of spatial autocorrelation. In this study, a standardized Euclidean distance matrix approach was used to accommodate this spatial effect. The results of the autocorrelation test of the binary spatial logistic model with the Euclidean distance matrix approach were able to accommodate the spatial effect.

Symmetric Convolutional and Adversarial Neural Network Enables Improved Mental Stress Classification From EEG

Electroencephalography (EEG) is widely used for mental stress classification, but effective feature extraction and transfer across subjects remain challenging due to its variability. In this paper, a novel deep neural network combining convolutional neural network (CNN) and adversarial theory, named symmetric deep convolutional adversarial network (SDCAN), is proposed for stress classification based on EEG. The adversarial inference is introduced to automatically capture invariant and discriminative features from raw EEG, which aims to improve the classification accuracy and generalization ability across subjects. Experiments were conducted with 22 human subjects, where each participant's stress was induced by the Trier Social Stress Test paradigm while EEG was collected. Stress states were then calibrated into four or five stages according to the changing trend of salivary cortisol concentration. The results show that the proposed network achieves improved accuracies of 87.62% and 81.45% on the classification of four and five stages, respectively, compared to conventional CNN methods. Euclidean space data alignment approach (EA) was applied and the improved generalization ability of EA-SDCAN across subjects was also validated via the leave-one-subject-out-cross-validation, with the accuracies of four and five stages being 60.52% and 48.17%, respectively. These findings indicate that the proposed SDCAN network is more feasible and effective for classifying the stages of mental stress based on EEG compared with other conventional methods.

Phase transitions in 4D Gauss–Bonnet–de Sitter black holes

We investigate thermodynamic aspects of black holes in the recently formulated four dimensional Gauss-Bonnet theory of gravity, focusing on its asymptotically de Sitter ($\Lambda>0$) solutions. We take a Euclidean path integral approach, where thermodynamic data is fixed at a finite radius `cavity' outside the black hole to achieve equilibrium in the presence of the cosmological horizon. Working in the extended phase space where the cosmological constant is treated as a thermodynamic pressure, we study the phase structure of both uncharged and charged solutions, uncovering a wealth of phenomena. In the uncharged case, black holes are found to undergo either the standard Hawking-Page-like transition to empty de Sitter space or a small-large transition (akin to those seen in charged AdS black holes in pure Einstein gravity) depending on the pressure. We also find a triple point where the radiation, small, and large black hole phases coexist, and a zeroth-order phase transition within a narrow range of Gauss-Bonnet coupling parameter. Reentrant phase transitions between radiation and a small black hole also exist inside a certain parameter range. Similar phenomena are found in the charged case, with the charge parameter playing a role analogous to the Gauss-Bonnet coupling parameter in determining the phase structure.

Optimization of Extraction Parameters of Ethanol Extracts of Propolis Samples Using Artificial Neural Network and Moth-Flame Optimization Algorithm

Abstract In this study, the optimum values of propolis ethanol extracts parameters were determined with the use of single and multi-objective optimization procedures. The euclidean distance approach was used in the multi-objective optimization process. Firstly, propolis was extracted using water with ethanol contents 40, 50, 60, 70 and 80% for 8, 10, 12, 16, 20 and 24 h. Then, total phenolic content (TPC) and ferric reducing antioxidant power (FRAP) activities of all extracts were determined. With the obtained data a prediction model was produced with the use of artificial neural networks (ANN), and optimization was performed using a moth-flame (MFO) algorithm. The best prediction models for the TPC and FRAP were observed in 2-5-1 and 2-5-1 network architecture with the mean absolute percentage error (MAPE) values, 5.126 and 2.451%, respectively. For maximum TPC, the extraction parameters were determined as ethanol content 57.5% and extraction time 13.56 h. To maximize FRAP, the optimized extraction parameters were ethanol content 72.03% and extraction time 18.04 h. The optimum extraction conditions for both maximum values of the studied assays were ethanol content 70.03% and extraction time 16.93 h. The study concluded that the integrated ANN and MFO algorithm system can be used in single and multi-objective optimization of extraction parameters. The established optimization model can save time, money, labor and energy.

Lorentzian vacuum transitions: Open or closed universes?

We consider the generalisation of quantum tunneling transitions in the WKB approximation to the time-independent functional Schr\"odinger and Wheeler-DeWitt equations. Following a Lorentzian approach, we compute the transition rates among different scalar field vacua and compare with those performed by Coleman and collaborators using the Euclidean approach. For gravity, we develop a general formalism for computing transition rates in Wheeler's superspace. This is then applied to computing decays in flat space and then to transitions in the presence of gravity. In the latter case we point out the complexities arising from having non-positive definite kinetic terms illustrating them in the simplified context of mini-superspace. This corresponds to a generalisation of the well-known `tunneling from nothing' scenarios. While we can obtain the leading term for the transitions obtained by Euclidean methods we also point out some differences and ambiguities. We show that there is no obstruction to keeping the spherically ($SO(4)$) symmetric closed slicing for the new vacuum after a de Sitter to de Sitter transition. We argue that this is the natural Lorentzian realisation of the Coleman-De Luccia instanton and that a closed universe is also obtained if the mini-superspace assumption is relaxed. This is contrary to the open universe predicted by Coleman-De Luccia which relies on an analytic continuation performed after bubble nucleation. Our findings may have important cosmological implications related to the origin of inflation and to the string landscape. In particular, they question the widespread belief that evidence for a closed universe would rule out the string landscape.