Aggregation Distance Research Articles

An Objective Detection of Separation Scenario in Tropical Cyclone Trajectories Based On Ensemble Weather Forecast Data

AbstractIn ensemble weather forecast, tropical cyclone (TC) tracks sometimes group together into trajectories parting away from each other. The goal of this study is to propose an objective method, based on a robust clustering approach, to detect such separation scenarios in the Japan Meteorological Agency Meso‐scale Ensemble Prediction System (MEPS) for three TCs: “Dolphin” (2020), “Nepartak” (2021), and “Meari” (2022). Taking advantage of the independence of the density‐based spatial clustering of applications with noise algorithm to the prior choice of the number of clusters, we first describe an objective way to calculate the aggregation distance, by searching the most frequent Euclidean distance between all the tracks. The clustering is then applied to the forecasted tracks, for each initialization time of MEPS (every 6 hr). Separation scenarios exist when the number of clusters is greater than one.

Nonrandom foraging and resource distributions affect the relationships between host density, contact rates and parasite transmission.

Nonrandom foraging can cause animals to aggregate in resource dense areas, increasing host density, contact rates and pathogen transmission, but when should nonrandom foraging and resource distributions also have density-independent effects? Here, we used a factorial experiment with constant resource and host densities to quantify host contact rates across seven resource distributions. We also used an agent-based model to compare pathogen transmission when host movement was based on random foraging, optimal foraging or something between those states. Nonrandom foraging strongly depressed contact rates and transmission relative to the classic random movement assumptions used in most epidemiological models. Given nonrandom foraging in the agent-based model and experiment, contact rates and transmission increased with resource aggregation and average distance to resource patches due to increased host movement in search of resources. Overall, we describe three density-independent mechanisms by which host behaviour and resource distributions alter contact rate functions and pathogen transmission.

A Self-Assemble Supramolecular Film with Humidity Visualization Enabled by Clusteroluminescence.

Clusteroluminescence (CL) has recently gained significant attention due to its unique through-space interactions associated with a high dependence on the aggregation of subgroups. These distinct features could easily transform the stimuli into visual fluorescence and monitor the fluctuation of the environment but have not received sufficient attention before. In this work, supramolecular films are designed based on the neutralization reaction of anhydride groups and the self-assembly of dynamic covalent disulfide bonds in NaOH aqueous solution. The self-assembly of hydrophilic carboxylate chromophores and hydrophobic disulfide-containing five-membered rings could be observed by the variation of the aggregation state of carboxylate in CL. Furthermore, the dynamic cross-linking films obtained with water-sensitive carboxylate chromophores could alter the aggregation distance stimulated by surrounding water vapor, causing the emission wavelength to change from 534 to 508nm by varying the relative humidity. This work not only provides an approach to monitor the self-assembly of clusteroluminogens but also offers new strategies for designing stimuli-responsive materials that utilize the intrinsic features of CL.

Controlled assembly of NiCo-LDH nanosheets into diverse superstructures via concentration-dependent solvothermal method for advanced supercapacitors

The tunable layer structure, composition, and diverse morphologies of layered double hydroxides (LDHs) make them a favorable choice as pseudocapacitive electrode materials for supercapacitors. However, the severe agglomeration and low conductivity of nanosheets are detrimental to obtaining excellent electrochemical performance. In this work, a controllable assembly of nanosheets was achieved by adjusting the concentrations of Ni and Co salt solutions, resulting in a series of 3D-shaped NiCo-LDHs superstructures with diverse morphologies. The prepared structures were found to significantly reduce aggregation and electron transport distance. The results approved that the diameter, thickness and interlayer space of NiCo-LDHs nanosheets and the morphologies of their self-assembled superstructures were subtly controlled, and significantly impacted their electrochemical performance. A stretch trumpet shaped flower-like superstructure in NiCo-LDH-2 was constructed by the large and ultrathin nanosheets with much more accessible active sites and larger interlayer space, which can rapidly promote electron transfer and show the highest specific capacitance of 2265 F g−1 at 1 A g−1 and retention of 86.5% at 20 A g−1. The NiCo-LDHs//AC ASC based on NiCo-LDH-2 and active carbon (AC) reveals a high specific capacitance of 298 F g−1 at 1 A g−1 and an excellent energy density of 105.0 Wh kg−1 at the power density of 820.1 W kg−1. The current work presents a simple way for controlling the nanostructure of NiCo-LDHs in the development of high-performance supercapacitors.

Generalized q-rung picture linguistic aggregation operators and their application in decision making

The q-rung picture linguistic set (q-RPLS) is an effective tool for managing complex and unpredictable information by changing the parameter ‘q’ regarding hesitancy degree. In this article, we devise some generalized operational laws of q-RPLS in terms of the Archimedean t-norm and t-conorm. Based on the proposed generalized operations, we define two types of generalized aggregation operators, namely the q-rung picture linguistic averaging operator and the q-rung picture linguistic geometric operator, and study their relevant characteristics in-depth. With a view toward applications, we discuss certain specific cases of the proposed generalized aggregation operators with a range of parameter values. Furthermore, we explore q-rung picture linguistic distance measure and its required axioms. Then we put forward a technique for q-RPLSs based on the proposed aggregation operators and distance measure to solve multi-attribute decision-making (MADM) challenges with unknown weight information. At last, a practical example is presented to demonstrate the suggested approaches’ viability and to perform the sensitivity and comparison analysis.

An improved explainable artificial intelligence tool in healthcare for hospital recommendation

Artificial intelligence (AI) technologies have been widely applied in medicine and healthcare. Explainable AI (XAI) has been proposed to make AI applications more transparent and efficient. This study applies some simple cross-domain tools and techniques, including common expression (with linguistic terms), color management, traceable aggregation, and segmented distance diagrams, among others, to improve the explainability of AI applications in healthcare. Four applications of AI technologies in hospitals were used, and recommendations were studied to illustrate the applicability of the proposed methodology. The explainability of each AI application was evaluated before and after improvement for comparison. According to the experimental results, these AI-based hospital recommendation methods could be better explained by modifying their explanations using simple and cross-domain tools.

Complex intuitionistic fuzzy ordered weighted distance measure

Compared with the complex fuzzy set (CFS) and the intuitionistic fuzzy set (IFS), the complex intuitionistic fuzzy set (CIFS) can handle the two-dimensional and uncertain information simultaneously, and thus strengthen the capability of capturing useful information. In this paper, we consider the situation with complex intuitionistic fuzzy environment, where the importance of certain information is also taken into consideration. To deal with these situations, we develop the complex intuitionistic fuzzy ordered weighted distance (CIFOWD) measure and investigate its main properties. We find that the CIFOWD measure provides a parameterized family of aggregation distance measures, which includes, for example, complex intuitionistic fuzzy ordered weighted geometric distance (CIFOWGD) measure, complex intuitionistic fuzzy ordered weighted Hamming distance (CIFOWHD) measure, and complex intuitionistic fuzzy ordered weighted Euclidean distance (CIFOWED) measure as special types. The CIFOWD measure is capable of alleviating the influence of excessively large or excessively small deviations on the aggregation results via weight vector. Based on these distance measures, a multiple criteria group decision-making approach is presented under the CIFSs environment. An illustrative example regarding coronavirus vaccine selection on COVID-19 is taken for demonstrating the effectiveness of the proposed approach. Finally, the proposed results are compared with the results obtained from the existing methods.

An Integrated Two-Dimension Linguistic Intuitionistic Fuzzy Decision-Making Approach for Unmanned Aerial Vehicle Supplier Selection

With the rapid development of unmanned aerial vehicles (UAVs) and their applications in problems such as power line inspection, selecting the appropriate UAV supplier according to several sustainable attributes has attracted many interests. In this regard, an integrated multiattribute group decision-making (MAGDM) method based on the best-worst method (BWM) and MULTIMOORA method with two-dimension linguistic intuitionistic fuzzy variables (2DLIFVs) is proposed in this paper for the selection of UAV suppliers. First, the 2DLIFV is utilized to represent the uncertain, fuzzy, and linguistic evaluations of the experts on the evaluation attributes. Second, the two-dimension linguistic intuitionistic fuzzy BWM (2DLIF-BWM) is introduced to compute the weights of the attributes. Then, a novel expert weight calculation method that combines the uncertainty degree and consensus degree of the experts is introduced. Next, the 2DLIF-MULTIMOORA method is proposed, where the aggregation operators and distance measures of the 2DLIFVs are used to determine the ranking results of different alternatives. Finally, a real case of selecting a sustainable UAV supplier for power line inspection is presented to illustrate the process of the proposed method. The experimental results are further analyzed through sensitivity and comparative analyses to show the feasibility and effectiveness of the proposed method. From the results, it can be found that the proposed method can more flexibly represent the uncertain assessments while providing reasonable and reliable results.

Macrofauna Associated With a Rhodolith Bed at an Oceanic Island in the Eastern Tropical Pacific (Isla del Coco National Park, Costa Rica)

Rhodoliths are round calcareous red algae that form extensive beds and associated with them are a diverse suite of species. Rhodolith beds are among the least known coastal–marine ecosystems, and even less is understood about their associated flora and fauna. Here, we present an evaluation of the biodiversity larger than 500 μm associated with rhodoliths at Isla del Coco National Park, Costa Rica, an oceanic island in the Eastern Tropical Pacific, 500 km offshore of the mainland. This research determined the influence of rhodolith degree of aggregation (distance among individual rhodolith) as well as rhodolith complexity, volume, and mass in relation to the diversity, composition, and biomass of the associated fauna. A total of 145 taxa were collected in 60 rhodolith samples. Arthropods, polychaetes, and mollusks were the dominant taxa in terms of richness, and crustaceans + acari represented >50% of the total abundance. Five potentially new species were collected in this study. Collections identified 31 new records, with 20 of them being newly reported genera for Isla del Coco. Many of the organisms found were juveniles as well as adult stages bearing eggs, demonstrating the importance of this ecosystem. The faunal composition changed along the gradient of rhodolith aggregation. Moderately aggregated rhodoliths (separated by 5 to 10 cm) had the highest diversity, with the highest averages of taxon richness and total numerical abundance and the highest faunal biomass. There were more organisms in more complex rhodoliths; nevertheless, the complexity of the rhodolith did not affect the number of taxa or total organism biomass. Larger rhodoliths, in size and mass, favored higher amounts of taxa and organisms. The rhodolith bed studied is an example of the intermediate disturbance hypothesis because the highest value of biodiversity of invertebrates was at the moderate aggregation level of rhodoliths. In this density of rhodoliths, the fauna was less exposed to hard environmental conditions and prevented dominant species.

Fuel-Specific Aggregation of Active Fire Detections for Rapid Mapping of Forest Fire Perimeters in Mexico

Context and Background. Active fires have the potential to provide early estimates of fire perimeters, but there is a lack of information about the best active fire aggregation distances and how they can vary between fuel types, particularly in large areas of study under diverse climatic conditions. Objectives. The current study aimed at analyzing the effect of aggregation distances for mapping fire perimeters from active fires for contrasting fuel types and regions in Mexico. Materials and Methods. Detections of MODIS and VIIRS active fires from the period 2012–2018 were used to obtain perimeters of aggregated active fires (AGAF) at four aggregation distances (750, 1000, 1125, and 1500 m). AGAF perimeters were compared against MODIS MCD64A1 burned area for a total of 24 fuel types and regions covering all the forest area of Mexico. Results/findings. Optimum aggregation distances varied between fuel types and regions, with the longest aggregation distances observed for the most arid regions and fuel types dominated by shrubs and grasslands. Lowest aggregation distances were obtained in the regions and fuel types with the densest forest canopy and more humid climate. Purpose/Novelty. To our best knowledge, this study is the first to analyze the effect of fuel type on the optimum aggregation distance for mapping fire perimeters directly from aggregated active fires. The methodology presented here can be used operationally in Mexico and elsewhere, by accounting for fuel-specific aggregation distances, for improving rapid estimates of fire perimeters. These early fire perimeters could be potentially available in near-real time (at every satellite pass with a 12 h latency) in operational fire monitoring GIS systems to support rapid assessment of fire progression and fire suppression planning.

An Improved K-Means Algorithm Based on Evidence Distance

The main influencing factors of the clustering effect of the k-means algorithm are the selection of the initial clustering center and the distance measurement between the sample points. The traditional k-mean algorithm uses Euclidean distance to measure the distance between sample points, thus it suffers from low differentiation of attributes between sample points and is prone to local optimal solutions. For this feature, this paper proposes an improved k-means algorithm based on evidence distance. Firstly, the attribute values of sample points are modelled as the basic probability assignment (BPA) of sample points. Then, the traditional Euclidean distance is replaced by the evidence distance for measuring the distance between sample points, and finally k-means clustering is carried out using UCI data. Experimental comparisons are made with the traditional k-means algorithm, the k-means algorithm based on the aggregation distance parameter, and the Gaussian mixture model. The experimental results show that the improved k-means algorithm based on evidence distance proposed in this paper has a better clustering effect and the convergence of the algorithm is also better.

Characterization of Helicoverpa armigera spatial distribution in pigeonpea crop using geostatistical methods.

The gram pod borer, Helicoverpa armigera (Lepidoptera: Noctuidae) is an economically important pest of pigeonpea crop in India. Fixed plot surveys for H. armigera larvae were carried out at 28 pigeonpea fields located in the Southern Plateau and Hills agro-climatic zone of India for three crop seasons (nine sampling weeks per season). The spatiotemporal dynamics of H. armigera larvae in the experimental area (=Hanamkonda) was analysed using geostatistics tools, namely a variogram and Voronoi diagram, and H. armigera larval distribution patterns were further characterized and mapped. A significant difference in H. armigera larval incidence was noticed between sampling weeks, with greater larval incidence observed between 26 November and 2 December. Pod formation phenophase (Meteorological Standard Week 44) of pigeonpea favoured the initial H. armigera larval incidence. Variogram analysis revealed moderate to strong larval aggregation (spatial dependence) of H. armigera in all nine sampling weeks. Based on the range value of the variogram, the average aggregation distance of H. armigera larvae in pigeonpea was estimated to be 2425.48 m. Voronoi diagrams illustrated the spatial heterogeneity of H. armigera larva between sampling weeks, which can be linked to availability of food sources. This study witnessed intrapopulation variability in H. armigera larvae associated with geographical space and temporal patterns. Based on our findings, a sampling distance of 2425.48 m may be used in larger pigeonpea fields (experimental area) to reduce scouting fatigue. The interpolated maps generated in this study may be of value in developing effective H. armigera larva monitoring and management tools in pigeonpea crop.

Using Non-dominated Sorting Particle Swarm Optimization Algorithm II for Bi-objective Flow Shop Scheduling Problems

A hybrid particulate swarm optimization (hybrid) combination of an optimization algorithm of the particle swarm and a variable neighborhood search algorithm is proposed for the multi-objective permutation flow shop scheduling problem (PFSP) with the smallest cumulative completion time and the smallest total flow time. Algorithm for hybrid particulate swarm optimization (HPSO) is applied to maintain a fair combination of centralized search with decentralized search. The Nawaz-Enscore-Ham )NEH) heuristic algorithm in this hybrid algorithm is used to initialize populations in order to improve the efficiency of the initial solution. The method design is based on ascending order (ranked-order-value, ROV), applying the continuous PSO algorithm to the PFSP, introducing the external archive set storage Pareto solution, and using a hybrid strategy that combines strong dominance and aggregation distance to ensure the distribution of the solution set. We adopted the Sigma method and the roulette method, based on the aggregation distance, to select the global optimal solution. A variable neighborhood search algorithm was proposed to further search the Pareto solution in the external set. The suggested hybrid algorithm was used to solve the Taillard test set and equate the test results with the SPEA2 algorithm to check the scheduling algorithm’s efficacy.

Traffic noise assessment based on mobile measurements

Continuous noise monitoring based on mobile systems could provide a quick feedback to assess the effect of policies implemented by authorities to mitigate noise pollution. The present research verifies that mobile measurements taken along a main street and aggregated in time and space can accurately estimate noise levels at static points. As a consequence, mobile sensors would be suitable to build, and continuously update, noise maps. Furthermore, the experiment computes the optimum aggregation distance of the mobile measurements.To perform the mobile noise measurements, a low-cost noise sensor with an integrated GPS was mounted on a bicycle. One hour worth of measurements was taken along a main avenue with the mobile receiver simultaneously to 6 static measurement points. For the mobile receiver, the LAeq was computed aggregating samples within a radius from 1 m to 100 m around the static measurement points. Then, the error between the aggregated LAeq of the mobile and the static receivers for the same time period was computed. It is observed that the RMSE and the measurement uncertainties decrease as the aggregation distance increases, having a minimum at an aggregation radius of 33 m and reaching a stabilization due to the constant traffic of the studied street.

A novel decomposition-based multiobjective evolutionary algorithm using improved multiple adaptive dynamic selection strategies

In the last decade, the decomposition-based multiobjective optimization evolutionary algorithm (MOEA/D) has displayed promising performance when dealing with multiobjective optimization problems (MOPs). However, for some complex MOPs, the conventional MOEA/D often leads to the loss of population diversity in the iterative process, and the convergence performance of the population is weakened in the mean time. In this paper, a novel MOEA/D, based on improved multiple adaptive dynamic selection strategies and elite archive strategy (MOEA/D-IMA), is proposed to improve the population diversity and convergence. First, a novel differential evolution (DE) operator is constructed, which constitutes an operator pool with other DE operators. According to the search information of the current population, an adaptive dynamic selection strategy is proposed, which is used by MOEA/D-IMA to select a suitable DE operator to replace the simulated binary crossover (SBX) operator. Second, a parameter adaptive dynamic selection strategy is proposed to enhance the robustness of MOEA/D-IMA by using the information of population evolution state. Third, an elite archive strategy is introduced to improve the convergence and diversity of the population where mutual dominance of individuals and their aggregation distance is employed. Finally, the proposed MOEA/D-IMA is compared with several state-of-the-art algorithms on three suits of 18 test problems. Experimental results indicate that the proposed MOEA/D-IMA can significantly improve the optimization performance when coping with MOPs.

Effects of aggregation pheromone concentration and distance on the trapping of Rhyzopertha dominica (F.) (Coleoptera: Bostrychidae) adults

Rhyzopertha dominica male adults produce an aggregation pheromone that attracts both sexes. While many studies have tested the behavioral response of R. dominica adults to its aggregation pheromone, information on the distance of attraction and effective pheromone concentration are lacking. Therefore, the objectives of this study were to determine the recapture rate of R. dominica adults released at different distances from a pheromone-baited and pheromone + kairomone-baited trap, as well as the most effective pheromone concentration for R. dominica. Experiments were arranged as complete randomized design with four replicates. A commercial pitfall trap containing R. dominica pheromone alone or pheromone + kairomone was placed inside an experimental arena. Adult R. dominica were released at different distances from the trap and the adults captured were counted. A separate experiment was conducted using pitfall traps containing different concentrations of the aggregation pheromone placed inside the experimental arena. Adult R. dominica were released 60 cm away from the pitfall trap and the recaptured adults were recorded. The trapping efficiency was higher when the trap contained both the pheromone and kairomone than the pheromone alone. Trap capture was highest when the beetles were released at distances up to 70 cm and at a concentration of 100 μL/1 m2. Our findings suggest that food facility managers should take into account the effective pheromone concentration and distance of attraction, while also strongly consider including food kairomones in traps when developing monitoring programs for R. dominica.

Near Real-Time Automated Early Mapping of the Perimeter of Large Forest Fires from the Aggregation of VIIRS and MODIS Active Fires in Mexico

In contrast with current operational products of burned area, which are generally available one month after the fire, active fires are readily available, with potential application for early evaluation of approximate fire perimeters to support fire management decision making in near real time. While previous coarse-scale studies have focused on relating the number of active fires to a burned area, some local-scale studies have proposed the spatial aggregation of active fires to directly obtain early estimate perimeters from active fires. Nevertheless, further analysis of this latter technique, including the definition of aggregation distance and large-scale testing, is still required. There is a need for studies that evaluate the potential of active fire aggregation for rapid initial fire perimeter delineation, particularly taking advantage of the improved spatial resolution of the Visible Infrared Imaging Radiometer (VIIRS) 375 m, over large areas and long periods of study. The current study tested the use of convex hull algorithms for deriving coarse-scale perimeters from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) active fire detections, compared against the mapped perimeter of the MODIS collection 6 (MCD64A1) burned area. We analyzed the effect of aggregation distance (750, 1000, 1125 and 1500 m) on the relationships of active fire perimeters with MCD64A1, for both individual fire perimeter prediction and total burned area estimation, for the period 2012–2108 in Mexico. The aggregation of active fire detections from MODIS and VIIRS demonstrated a potential to offer coarse-scale early estimates of the perimeters of large fires, which can be available to support fire monitoring and management in near real time. Total burned area predicted from aggregated active fires followed the same temporal behavior as the standard MCD64A1 burned area, with potential to also account for the role of smaller fires detected by the thermal anomalies. The proposed methodology, based on easily available algorithms of point aggregation, is susceptible to be utilized both for near real-time and historical fire perimeter evaluation elsewhere. Future studies might test active fires aggregation between regions or biomes with contrasting fuel characteristics and human activity patterns against medium resolution (e.g., Landsat and Sentinel) fire perimeters. Furthermore, coarse-scale active fire perimeters might be utilized to locate areas where such higher-resolution imagery can be downloaded to improve the evaluation of fire extent and impact.

Single-Valued Neutrosophic Linguistic-Induced Aggregation Distance Measures and Their Application in Investment Multiple Attribute Group Decision Making

This paper studied the single-valued neutrosophic linguistic distance measures based on the induced aggregation method. Firstly, we proposed a single-valued neutrosophic linguistic-induced ordered weighted averaging distance (SVNLIOWAD) measure, which is a new extension of the existing distance measures based on the induced aggregation view. Then, based on the proposed SVNLIOWAD, a novel induced distance for single-valued neutrosophic linguistic sets, namely the single-valued neutrosophic linguistic weighted induced ordered weighted averaging distance (SVNLWIOWAD), was developed to eliminate the defects of the existing methods. The relationship between the two proposed distance measures was also explored. A multiple attribute group decision making (MAGDM) model was further presented based on the proposed SVNLWIOWAD measure. Finally, a numerical example concerning an investment selection problem was provided to demonstrate the usefulness of the proposed method under a single-valued neutrosophic linguistic environment and, then, a comparison analysis was carried out to verify the flexibility and effectiveness of the proposed work.

Multisource Aggregation Search and Scheduling for Remote Sensing Data Cluster

Multisource aggregation (MSA) is an important function in the remote sensing data processing. In this letter, we propose and study a novel MSA search and scheduling problem to improve the performance of remote sensing data cluster. Given a static spatial network, a set of query points $Q$ , and a set of data locations $O$ (candidates for cluster centers), the MSA function retrieves the data location with the minimum aggregation distance (the sum of the distances to all query points). We believe that such function plays an important role in remote sensing data cluster and classification. The MSA problem faces two challenges: 1) how to prune the search space effectively and retrieve the results of MSA in real time and 2) how to schedule multiple query sources during search processing. To overcome these challenges, we make the following contributions. First, upper and lower bounds on aggregation distance are defined to prune the search space effectively. Second, each query source is given a priority label, and a best-first scheduling strategy is developed to further enhance the query performance. Finally, we conduct extensive experiments on real and synthetic data sets to verify the high performance of the developed algorithms.

An uncertain Z-number multicriteria group decision-making method with cloud models

This study focuses on developing an innovative multicriteria group decision-making (MCGDM) method to handle practical problems based on information description, information fusion and information measure. Given that various uncertain data exist in the process of managing problems, uncertain Z-numbers are introduced to represent evaluation information and measure information reliability simultaneously. Processing uncertain information requires the effective support of reliable tools; then, cloud models can be used to address uncertain Z-number information. First, uncertain Z-numbers are converted, and a novel concept of Z-trapezium-normal clouds (ZTNCs) is introduced. Next, the operations, power aggregation operators, and distance and likelihood measures of ZTNCs are defined. Subsequently, the likelihood power aggregation operator and the priority vector of the likelihood matrix are proposed. Moreover, a new MCGDM method is developed by combining the two kinds of proposed power aggregation operators with the likelihood measure. Finally, an illustrative example of new energy evaluation is provided to demonstrate the developed method, and its feasibility and validity are further verified by a comparative analysis with other existing methods.