Distance Criterion Research Articles

Effect of statistical positional correlation on the radiative property investigation of dispersed particulate medium

Spectral radiative transfer between particles of dispersed particulate medium is prevalent in various fields, which may be affected by particle distribution when considering dependent scattering effect (DSE). Meanwhile, statistical positional correlation (SPC) describes the possible existing order of particle distribution in the spatial variation for disordered dispersed particulate medium. SPC plays a pivotal role in the transformation of radiative transfer regimes between particles. However, the existing theory lacks a precise criterion for describing SPC and fails to comprehensively consider key factors within SPC influencing radiative transfer regimes, such as mean distance, relative distance, standard deviation, cluster, and aggregation. To achieve more representative and accurate results of radiative property calculations while minimizing computational resources, we proposed MRSDL (Mean distance, Relative distance, Standard deviation, Density-based clustering, and Line matrix) criterion combined with particle swarm optimization (PSO) for characterizing and regulating SPC. Moreover, to further achieve the statistical averaging of radiative properties precisely, the multiple sphere T-matrix (MSTM) method is combined to eliminate the random fluctuations of radiative properties caused by SPC. Compared to the conventional method, the method by authors can decrease error between experimental and calculation data from 41.23 % to 5.32 %, when considering the effect of SPC on the radiative property.

A quantitative assessment of GeoNet earthquake location quality in Aotearoa New Zealand

ABSTRACT Tens of thousands of earthquakes are located by GeoNet across Aotearoa every year. However, spatial variability – both in station coverage and earthquake distributions – produce heterogeneity in earthquake location quality throughout the GeoNet catalogue. Here we consider simple, established criteria (including station azimuthal coverage, minimum station distance, phase arrival coverage and fixed location criteria) to score earthquakes on a network location Quality Score (QS) scale of QS0 (unconstrained) to QS6 (best constrained). Significant variation in QS exists nationwide; 48% and 20% of earthquakes score QS6 for the North and South Islands respectively. The Hawkes Bay region scores highest (68% QS6) closely followed by other Hikurangi regions. However, several regions score extremely poorly, with Fiordland, West Coast, Nelson, Otago-Southland and Auckland-Northland regions having ≤5% QS6 events and >75% of events scoring QS3 or lower. Low QS mostly arises from the failure of minimum distance criteria, whereby the closest station is too far from the earthquake to provide sufficient depth control. Our analysis quantitatively assesses the impact of network heterogeneity on GeoNet earthquake catalogue location quality to inform end users. This work also provides motivation for the expansion of the weak-motion network in critically under-instrumented regions, especially those with high seismic hazard.

Investigation into Safety Regulation of Entertainment Venues Based on Big Data Label Analysis

The recreational escape rooms have recently emerged as a rapidly growing and widely embraced form of consumer entertainment. However, the industry’s expansion has brought forth certain challenges, notably the lack of authoritative oversight, which has led to issues such as piracy and theme infringement. To address these concerns, this study explores the management complexities of immersive entertainment venues from the perspective of responsive regulation. The study begins by abstracting a dataset of online cultural products related to entertainment venues into a complex network using specific measurement standards. A method employing the K-means clustering algorithm is then proposed to partition the associative structure of this complex network. The K-means clustering algorithm is particularly suitable for this task due to its efficiency in handling large-scale data, strong scalability, and ability to quickly and effectively group network nodes based on a defined objective function. Additionally, the algorithm allows for flexible adjustment of the number of clusters according to specific needs. Furthermore, the study enhances and tests the Practical Byzantine Fault Tolerance (PBFT) algorithm to validate its effectiveness and practicality. The findings reveal that when the distance criterion value is set at a=2, the improved PBFT algorithm exhibits exceptional performance. This discovery significantly enhances the security and control measures in immersive entertainment venues, enriching the theoretical framework related to administrative regulation and providing crucial theoretical resources for future legislative efforts. Finally, the study presents policy recommendations to strengthen the regulation of immersive entertainment venues. This study offers effective technical support for optimizing consumer market management measures and contributes to the development of new entertainment venues.

Strongly Coupled Simulation of Magnetic Rigid Bodies

AbstractWe present a strongly coupled method for the robust simulation of linear magnetic rigid bodies. Our approach describes the magnetic effects as part of an incremental potential function. This potential is inserted into the reformulation of the equations of motion for rigid bodies as an optimization problem. For handling collision and friction, we lean on the Incremental Potential Contact (IPC) method. Furthermore, we provide a novel, hybrid explicit / implicit time integration scheme for the magnetic potential based on a distance criterion. This reduces the fill‐in of the energy Hessian in cases where the change in magnetic potential energy is small, leading to a simulation speedup without compromising the stability of the system. The resulting system yields a strongly coupled method for the robust simulation of magnetic effects. We showcase the robustness in theory by analyzing the behavior of the magnetic attraction against the contact resolution. Furthermore, we display stability in practice by simulating exceedingly strong and arbitrarily shaped magnets. The results are free of artifacts like bouncing for time step sizes larger than with the equivalent weakly coupled approach. Finally, we showcase the utility of our method in different scenarios with complex joints and numerous magnets.

Optimizing energy efficiency and routing in wireless sensor networks through genetic algorithm-based cluster head selection in a grid-based topology

Wireless sensor networks (WSNs) struggle with energy efficiency because of limited node power. This paper presents an approach that uses evolutionary algorithms to choose the Cluster Head (CH) and optimize routing in wireless sensor networks (WSNs) using grid-based topologies. The proposed method repeatedly develops solutions based on criteria for node density, distance, and energy level by using the evolutionary capabilities of the genetic algorithm. A fitness function that considers latency, coverage, and energy efficiency is used to evaluate the solutions. The process selects CHs dynamically and uses GA-guided optimization to construct paths. Simulation results indicate improved network performance and energy efficiency over existing protocols. Evolutionary algorithm integration enables flexibility and optimization for energy-efficient CH selection and routing in WSNs with a grid-based design.

Space-filling designs on Riemannian manifolds

This paper proposes a new approach to generating space-filling designs over Riemannian manifolds by using a Hilbert curve. Different from ordinary Euclidean spaces, a novel transformation is constructed to link the uniform distribution over a Riemannian manifold and that over its parameter space. Using this transformation, the uniformity of the design points in the sense of Riemannian volume measure can be guaranteed by the intrinsic measure preserving property of the Hilbert curve. It is proved that these generated designs are not only asymptotically optimal under minimax and maximin distance criteria, but also perform well in minimizing the Wasserstein distance from the target distribution and controlling the estimation error in numerical integration. Furthermore, an efficient algorithm is developed for numerical generation of these space-filling designs. The advantages of the new approach are verified through numerical simulations.

К безопасности труда при транспортировании гидросмесей по трубопроводу

The article describes the technology of pipeline transportation of hydraulic mixtures for stowing at underground mining. The relevance of optimization of the hydraulic mixture transportation processes is based on the fact that filling the excavated space during its abandonment is the main factor of injury prevention among workers and the integrity of industrial facilities. The aim of the study is to detail the hardening mixture transportation methods for increased distance compared to the reached distance. The maximum damage factor is a rockslide into the open excavated space; therefore, the delivery of stowing pulp to the location of use minimizes the hazard for miners. The advantages of the proposed solution are substantiated by the adopted study methods. The study results can be used for the development of a set of protective measures. The applied mixture transportation methods have been analyzed. The practices of hydraulic mixture transportation for the distance of 2.5 km, which exceeded the best global achievements, have been described. Models of the description of hydraulic mixture transportation have been provided including the models obtained by calculations and experimental pressure loss. The operation schedules in forced mode for pipeline transport have been provided. It has been demonstrated that the optimization of aspects of the issue is specifically relevant for the intensification of processes of metal deposit development. The outcomes of the experimental study can be used when involving accumulated washery refuse in the treatment. It is recommended to determine the parameters of efficient delivery of hydraulic mixtures in vibration-induced mode by modelling in accordance with the criteria of distance and reliability of transportation. The recommendations on the use of hydraulic mixture delivery technology in forced mode can be required when developing mineral deposits by open and underground methods and training highly qualified personnel.

Stipulations Forthe Tofu Factory Location by Employing The AHP Method

The best qualifiedtofu will win the competition, so geological and climate citeria are considered the main priorities based on the analysis results involving AHP to determine the tofu factorylocation. Tofu factories are assigned as one of the small and medium industries and determining their locations greatly influences the sustainability of such industries since the determination not only greatly brings positive impacts on the factoryconstruction, but also reduces economic problems in the future. This research was aimed at specifying the priority criteria percentage in the locationselection; therefore, the research applied the use of AHP to determine them. The research found that the percentage of the next priority might refer to the availability of labors(26.94%), the criteria for market and marketing distance (14.93%), the criteria for transportation facilities (11.36%), the criteria for supporting facilities and utilities (7.08%), and finally the distance criteria fromthe location to the raw material suppliers. The distance to the raw material suppliers is very much prioritized in stipulating the location because the supply of raw materials does not take place every day, for example, the main raw materials, such as,soybeans, can be ordered once a month. Other aspects of the criteria are relied on the level of importancewhich is the final choice for building a tofu factory objectively.

Adaptive circumferential distance location of the laser energy beam in the laser-assisted turning of Al/SiC metal matrix composites

Laser-assisted turning (LAT) involves locally heating a rotating workpiece using a focused laser beam before the removal of material. A key aspect in optimising productivity with laser-assisted turning is understanding the thermal relationship between laser heating, the improved material removal rate, and machinability. Consequently, in this paper, a thermal heating and laser-assisted turning finite element model and experiments were conducted to assess the machinability of an Al/SiCp MMC workpiece, considering the circumferential location of the laser beam from the cutting point. The results confirm that laser power and cutting velocity influence the temperature profile from the laser spot to the tool point and the heat-affected depth. Positioning the cutting tool closer to the laser spot effectively reduces the Von Mises stress during cutting at higher cutting temperatures. At the same time, the experiment indicates an increased risk of directly heating the tool, which can affect the integrity of the cutting tool. The work further reveals that at specified cutting velocities, lower specific cutting energy improves the tool condition and surface quality of the machined parts. Based on a range of material removal rates and laser-specific energy density, a new criterion for optimal laser-tool circumferential distance was determined. Establishing this distance can act as a guide for the laser-assisted turning of Al/SiCp metal matrix composites and potentially other materials.

A Novel Hierarchical High-Dimensional Unsupervised Active Learning Method

This paper processes a novel hierarchical high-dimensional clustering algorithm based on the Active Learning Method (ALM), which is a fuzzy-learning algorithm. The hierarchical part of the algorithm is composed of two phases: divisible and agglomerative. The divisible phase, a zooming-in-process, searches for sub-clusters in already-found clusters hierarchically. At each level of the hierarchy, the clusters are found by an ensemble clustering method based on the density of data. This part of the algorithm blurs each data point as multiple one-dimensional fuzzy membership functions called ink-drop patterns; then, it accumulates the ink-drop patterns of all data points on every dimension separately. Next, it performs one-dimensional density partitioning to produce an ensemble of clustering solutions; after that, combining the results is done based on a novel consensus method with the aid of prime numbers. An agglomerative phase is a bottom-up approach that merges clusters based on a novel distance metric, named K2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${K}^{2}$$\\end{document}-nearest neighbor. The algorithm is named as the Hierarchical High-Dimensional Unsupervised Active Learning Method (HiDUALM) and is explained in more detail throughout this paper. Although the classical clustering methods are not suitable for high-dimensional data clustering, the proposed method solves the problems related to speed and memory using ensemble learning, while due to its hierarchy and the use of different distance criteria, different levels of the cluster provide the clause. Experiments on synthetic and real-world datasets are presented to show the effectiveness of the proposed-clustering algorithm.

Energy efficient cluster-based routing protocol for WSN using multi-strategy fusion snake optimizer and minimum spanning tree

In recent years, the widespread adoption of wireless sensor networks (WSN) has resulted in the growing integration of the internet of things (IoT). However, WSN encounters limitations related to energy and sensor node lifespan, making the development of an efficient routing protocol a critical concern. Cluster technology offers a promising solution to this challenge. This study introduces a novel cluster routing protocol for WSN. The system selects cluster heads and relay nodes utilizing the multi-strategy fusion snake optimizer (MSSO) and employs the minimum spanning tree algorithm for inter-cluster routing planning, thereby extending the system’s lifecycle and conserving network energy. In pursuit of an optimal clustering scheme, the paper also introduces tactics involving dynamic parameter updating, adaptive alpha mutation, and bi-directional search optimization within MSSO. These techniques significantly increase the algorithm convergence speed and expand the available search space. Furthermore, a novel efficient clustering routing model for WSN is presented. The model generates different objective functions for selecting cluster heads and relay nodes, considering factors such as location, energy, base station distance, intra-cluster compactness, inter-cluster separation, and other relevant criteria. When selecting cluster heads, the fuzzy c-means (FCM) algorithm is integrated into MSSO to improve the optimization performance of the algorithm. When planning inter-cluster routing, the next hop node is selected for the relay node based on distance, residual energy, and direction.The experimental results demonstrate that the proposed protocol reduces energy consumption by at least 26.64% compared to other cluster routing protocols including LEACH, ESO, EEWC, GWO, and EECHS-ISSADE. Additionally, it increases the network lifetime of WSN by at least 25.84%, extends the stable period by at least 52.43%, and boosts the network throughput by at least 40.99%.

A case study of displacement, stress condition and failure criterion of surrounding rock in deep super-large section double chambers in the Longgu Coal Mine, Shandong, China

To investigate the stability of a super-large section chamber group, an analysis was conducted based on the in-situ geological conditions of a super-large section chamber group utilized as a coal gangue separation system at the Longgu Coal Mine. Field measurement and numerical simulation were employed to analyze the failure area and stress state of the surrounding rock under varying chamber spacings. The results indicate that the extent of the plastic zone significantly expands when the spacing between chambers is less than 2.0 times the chamber width. When the distance between chambers is 1.5 times the chamber width, it results in the rock pillars being entirely within the plastic zone. As the chamber spacing decreases, the tangential stress within the rock pillar range increases. When the chamber spacing is 2.5, 2.0, and 1.5 times the chamber width, the maximum tangential stress is 1.19, 1.46, and 1.18 times that in the case of a single chamber, respectively. Based on the displacement analysis, it was observed that as the distance between the chambers decreases, there is a notable increase in the displacement of the pillar sides and chamber top, indicating a higher risk of collapse. Integrating the plastic area and stress analysis allows for the categorization of the rock pillar area into four sections: the broken area, loose area, stable area, and firm area. Drawing upon the theoretical solution of the plastic zone of a circular chamber and the equivalent radius method, an approximate solution for the plastic zone of a non-circular chamber has been provided. Furthermore, the minimum reasonable spacing between chambers in a super-large section chamber group is provided as a distance criterion for the failure of a double chamber.

The NEDC-GTOPSIS Node Influence Evaluation Algorithm Based on Multi-Layer Heterogeneous Classroom Networks

To address the deficiency in the analysis of individual students within existing research on in-classroom social networks and the constraints of traditional centrality metrics in identifying influential nodes, this paper presents the NEDC-GTOPSIS evaluation method for evaluating node influence in multi-layer heterogeneous networks. Initially, students' friendship, interaction, and attribute information are leveraged to compute neighborhood overlap and attribute similarity between nodes, to construct the Composite Relationship Network. Subsequently, the Seat Similarity Network is constructed by applying the Nearest-Neighbor Effective Distance Criterion to compute seat similarity across various class sessions among nodes. Finally, the structure characteristics of two networks serve as influence decision indicators, and the GRA-TOPSIS algorithm, based on the combined weight method, evaluates nodes' influence. Experiments demonstrate that, compared to traditional single-layer relational networks and classical algorithms, this method can more effectively assess influential student nodes.

Fracture of Epoxy Networks Using Atomistic Simulations.

Predicting fracture properties through all-atomistic simulations poses challenges due to classical force field limitations in breaking covalent bonds and the computational demands of reactive force fields like ReaxFF. In addressing this, we propose a scale-bridging method for forecasting the fracture behavior of highly cross-linked epoxy combining classical force fields, the LAMMPS package REACTER, and for bond breaking a parameter based on experimental distance criterion. In our analysis, we anticipate the macroscopic fracture energy GC of the epoxy network through the application of a continuum fracture mechanics model developed for fibrils. In addition, we extract the value of the stress intensity factor KI. This modeling approach is specifically implemented for a frequently used epoxy system that consists of bisphenol F and DETDA hardener. Notably, our results demonstrate a robust correlation with existing literature and experimental studies. Moreover, our approach boasts a substantial computational time advantage, facilitating calculations that are significantly faster compared to those performed using reactive force fields.

Column expanded Latin hypercube designs

Maximin distance designs and orthogonal designs are extensively applied in computer experiments, but the construction of such designs is challenging, especially under the maximin distance criterion. In this paper, by adding columns to a fold-over optimal maximin L2-distance Latin hypercube design (LHD), we construct a class of LHDs, called column expanded LHDs, which are nearly optimal under both the maximin L2-distance and orthogonality criteria. The advantage of the proposed method is that the resulting designs have flexible numbers of factors without computer search. Detailed comparisons with existing LHDs show that the constructed LHDs have larger minimum distances between design points and smaller correlation coefficients between distinct columns.

Adaptive crossover-based marine predators algorithm for global optimization problems

Abstract The Marine Predators Algorithm (MPA) is a swarm intelligence algorithm developed based on the foraging behavior of the ocean’s predators. This algorithm has drawbacks including, insufficient population diversity, leading to trapping in local optima and poor convergence. To mitigate these drawbacks, this paper introduces an enhanced MPA based on Adaptive Sampling with Maximin Distance Criterion (AM) and the horizontal and vertical crossover operators – i.e., Adaptive Crossover-based MPA (AC-MPA). The AM approach is used to generate diverse and well-distributed candidate solutions. Whereas the horizontal and vertical crossover operators maintain the population diversity during the search process. The performance of AC-MPA was tested using 51 benchmark functions from CEC2017, CEC2020, and CEC2022, with varying degrees of dimensionality, and the findings are compared with those of its basic version, variants, and numerous well-established metaheuristics. Additionally, 11 engineering optimization problems were utilized to verify the capabilities of the AC-MPA in handling real-world optimization problems. The findings clearly show that AC-MPA performs well in terms of its solution accuracy, convergence, and robustness. Furthermore, the proposed algorithm demonstrates considerable advantages in solving engineering problems, proving its effectiveness and adaptability.

Molecular Dynamics Simulation of Cumulative Microscopic Damage in a Thermosetting Polymer under Cyclic Loading.

To develop durable composite materials, it is crucial to elucidate the correlation between nanoscale damage in thermosetting resins and the degradation of their mechanical properties. This study aims to investigate this correlation by performing cyclic loading tests on the cross-linked structure of diglycidyl ether bisphenol A (DGEBA) and 4,4'-diaminodiphenyl sulfone (44-DDS) using all-atom molecular dynamics (MD) simulations. To accurately represent the nanoscale damage in MD simulations, a bond dissociation algorithm based on interatomic distance criteria is applied, and three characteristics are used to quantify the microscopic damage: stress-strain curves, entropy generation, and the formation of voids. As a result, the number of covalent bond dissociations increases with both the cyclic loading and its amplitude, resulting in higher entropy generation and void formation, causing the material to exhibit inelastic behavior. Furthermore, our findings indicate the occurrence of a microscopic degradation process in the cross-linked polymer: Initially, covalent bonds align with the direction of the applied load. Subsequently, tensioned covalent bonds sequentially break, resulting in significant void formation. Consequently, the stress-strain curves exhibit nonlinear and inelastic behavior. Although our MD simulations employ straightforward criteria for covalent bond dissociation, they unveil a distinct correlation between the number of bond dissociations and microscale damage. Enhancing the algorithm holds promise for yielding more precise predictions of material degradation processes.

Mitigating uncertainty: A risk informed approach for deploying hydrogen refueling stations

Regulatory gaps and safety concerns induce uncertainty amongst stakeholders, increases investment risk and impedes the deployment of hydrogen refueling stations (HRS). In this study, a risk-informed approach was employed on a potential HRS in Canada, to optimize separation distances, and identify regulatory gaps. This risk assessment improves upon previous ones that assumed uniform hydrogen leak sizes throughout the entire HRS. This analysis considers variable leak sizes based on the varying pipe flow area in different parts of the HRS like the tube-trailer area, hydrogen system area and the dispenser area. For each area, the potential leak sizes of 0.1%, 1%, 10%, and 100% of the pipe flow area were evaluated to cover a range of possible leaks. Leak frequency for 1% and 10% leak sizes were found to be 2.38E-02/year and 2.68E-03/year indicating relatively smaller difference as compared to other leak sizes. Leak frequency and consequence distances were assessed using HyRAM+ (V5.0) and Safeti (V8.9) respectively. Separation distances, which refer to the required spacing measured from the hydrogen process areas beyond which the risks are considered tolerable, were assessed for the tube-trailer area, storage area, and dispenser area at 350 bar and 700 bar dispensing pressures. Results indicated that 700 bar scenario requires substantially larger separation distances up to 39.1 m due to extended harm distances at lower temperatures. This study proposes safety barriers which reduced jet and flash fire frequencies by 100 and 20 events/year, respectively, that enables decreasing separation distances for each area. The comprehensive evaluation of separation distances generates important insights into separation distance criteria and guidelines, addressing the identified gap in existing codes and standards. While the study focuses on a potential HRS in Canada, the methodology, approach, and results presented are broadly applicable and transferable to similar contexts globally.

Strong orthogonal Latin hypercubes for computer experiments

Orthogonal Latin hypercubes are widely used for computer experiments. They achieve both orthogonality and the maximum one-dimensional stratification property. When two-factor (and higher-order) interactions are active, two- and three-dimensional stratifications are also important. Unfortunately, little is known about orthogonal Latin hypercubes with good two (and higher)–dimensional stratification properties. A method is proposed for constructing a new class of orthogonal Latin hypercubes whose columns can be partitioned into groups, such that the columns from different groups maintain two- and three-dimensional stratification properties. The proposed designs perform well under almost all popular criteria (e.g., the orthogonality, stratification, and maximin distance criterion). They are the most ideal designs for computer experiments. The construction method can be straightforward to implement, and the relevant theoretical supports are well established. The proposed strong orthogonal Latin hypercubes are tabulated for practical needs.

Exact and efficient wall distance pre-classification overset grid assembly for unstructured grids

In this paper, we present a pre-classification overset grid assembly (OGA) based on the wall distance criterion. In contrast to the conventional OGA, where donor search is preceded by point classification, the proposed methodology first performs point classification based on the wall distance criterion, and then performs donor search on the reduced set of cells. To robustly profile the given grid components, a wall distance evaluation is performed exactly by considering the full geometry of the discretized surface while maintaining efficiency. The efficiency of the algorithm is achieved by redefining the methodology into a bounded form and establishing a conservative correlation between the vertex-based and element-based distances through a rigorous examination of geometric relations. From these observations, wall distance calculation, which requires a tremendous amount of computation, is divided into sequential steps of vertex-based approximate calculation and element-based exact calculation, dramatically reducing the computation load. To better adapt to parallel environments, load balancing is presented, and additionally, some voxelization techniques are developed to efficiently process the required geometric data. Numerous test cases are analyzed to demonstrate the efficiency and robustness of the proposed algorithm in various situations. The proposed algorithm consistently outperforms the conventional OGA, and also proves its robustness in the presence of high proximity objects, which highlights its suitability for large-scale multi-body problems.