Gravity Rule Research Articles

Choosing the best way: how wild common marmosets travel to efficiently exploit resources

While foraging, animals have to find potential food sites, remember these sites, and plan the best navigation route. To deal with problems associated with foraging for multiple and patchy resources, primates may employ heuristic strategies to improve foraging success. Until now, no study has attempted to investigate experimentally the use of such strategies by a primate in a context involving foraging in large-scale space. Thus, we carried out an experimental field study that aimed to test if wild common marmosets (Callithrix jacchus) employ heuristic strategies to efficiently navigate through multiple feeding sites distributed in a large-scale space. In our experiment, we arranged four feeding platforms in a trapezoid configuration with up to 60 possible routes and observe marmosets’ decisions under two experimental conditions. In experimental condition I, all platforms contained the same amount of food; in experimental condition II, the platforms had different amounts of food. According to the number and arrangement of the platforms, we tested two heuristic strategies: the Nearest Neighbor Rule and the Gravity Rule. Our results revealed that wild common marmosets prefer to use routes consistent with a heuristic strategy more than expected by chance, regardless of food distribution. The findings also demonstrate that common marmosets seem to integrate different factors such as distance and quantity of food across multiple sites distributed over a large-scale space, employing a combination of heuristic strategies to select the most efficient routes available. In summary, our findings confirm our expectations and provide important insights into the spatial cognition of these small neotropical primates.

Lattice Structure and Spatial Network Models Incorporating into Simulating Human-Mediated Dispersal of the Western Conifer Seed Bug Populations in South Korea

The western conifer seed bug (WCSB), Leptoglossus occidentalis, has expanded rapidly in the southern peninsula of Korea since it was first reported in southeastern Korea in 2010. Two types of human-mediated passive movements were devised for modeling the rapid advancement of the pest population in this study: traffic effects and forest-product transportation. A lattice structure model (LSM) was developed to accommodate the traffic effects pertaining to the local area along with the natural population dynamics of the pest. Separately, a spatial network model (SNM) was constructed to present the passive movement of the WCSB because of forest-product transportation between all local areas in Korea. The gravity rule was applied to obtain the parameters for forest-product transportation between the local areas. LSM and SNM were linked to the two present types of passive movements in the model. The model simulated fast, linear advancement in a short period, compared with slow, circular advancement because of the conventional natural diffusion process of populations. Simulation results were comparable to field data observed in the southern peninsula of Korea, matching the rapid advancement of about 400 km to the north area (Seoul) from the south area (Changwon) within six years and expanding across the nation in 10 years. Possible saturation of populations was predicted in the 2020s if survival conditions for the WCSB were favorable and no control efforts were given in field conditions. Dispersal because of SNM notably surpassed the dispersal simulated by LSM when the WCSB population rapidly dispersed over a wide area. The Allee-effect and contribution ratio of SNM were the factors governing the rapid expansion of pest populations. The possibility of using the combined model was further discussed to address different types of human-mediated passive movements associated with population dynamics in forest pest dispersal.

A multi-robot task allocation algorithm based on universal gravity rules

A multi-robot task allocation algorithm based on universal gravity rules

Generalized Benders decomposition for competitive facility location with concave demand and zone-specialized variable attractiveness

We study a competitive facility location problem, in which a company enters a market where competitor’s facilities exist. Customers with elastic buying powers make choices following the gravity rule. Each facility, once open, has an intrinsic fixed attraction to customers. Besides, zone-specialized variable attractiveness can be provided to increase the total attraction of a facility to a specific customer zone. The objective of the company is to maximize profit by determining the locations of the facilities and the facility-customer pairwise attractiveness level, accounting for the expected revenue and the cost. The problem is formulated as a mixed-integer nonlinear program and subsequently solved by a tailored generalized Benders decomposition algorithm with tunable parameters. We then conduct extensive computational studies to demonstrate the efficiency of the algorithm. Finally, we analyze the solution structures under different scenarios and provide managerial implications for real-world applications.

A Color-Theory-Based Chromaticity Coordinates Tracking Strategy for LED Color-Mixing System

Based on the color theory, a precise chromaticity coordinates tracking strategy for the LED color-mixing system is proposed. The relationship between the resulting mixing color and the flux ratio of primary colors is derived according to the center of gravity rule on the CIE1931 chromaticity diagram. Based on this, the hysteresis control is introduced to minimize the color shift caused by aging and temperature effects without requiring any temperature sensor. A prototype with red–green–blue (RGB) strings has been built and evaluated. The experimental results show that the luminous output can be controlled between 100 and 600 lm with an error within ±2%. Any chromaticity coordinate within the triangle enclosed by the points corresponding to RGB can be controlled accurately and the color distance between the output coordinate and the reference coordinate in CIE1976 is less than 0.007 over the whole color gamut.

An End-to-End Task-Simplified and Anchor-Guided Deep Learning Framework for Image-Based Head Pose Estimation

Image-based Head Pose Estimation (HPE) from an arbitrary view is still challenging due to the complex imaging conditions as well as the intrinsic and extrinsic property of the faces. Different from existing HPE methods combining additional cues or tasks, this paper solves the HPE problem by relieving problem complexity. Our method integrates the deep Task-Simplification oriented Image Regularization (TSIR) module with the Anchor-Guided Pose Estimation (AGPE) module, and formulate the HPE problem into a unified end-to-end learning framework. In this paper, we define anchors as images that strictly obey the “gravity rule in camera”, which follows the assumption that camera coordinate of the vertical axis should always be consistent with that of the local head coordinate. We formulate image pair as the regularized image produced by TSIR along with its anchor counterpart, both of which are fed into the AGPE module for estimating fine-grained head poses. This paper also proposes an Anchor-Guided Pairwise Loss (AGPL), which describes the interdependent relevance of poses between each pair of images. The proposed method is evaluated and validated with sufficient experiments which show its effectiveness. Comprehensive experiments show that our approach outperforms the state-of-the-art image-based methods on both indoor and outdoor datasets.

Enhancement of Gravitational Search Algorithm using A Differential Mutation Operator and Its Application on Reconstructing Gene Regulatory Network

Gravitational Search Algorithm (GSA) is a recent stochastic search algorithm that is inspired from the concepts of gravity rule and law of motion in physics. Despite its success and attractiveness, it has some coefficients and parameters that should be properly tuned to improve its performance. This paper studies the performance of GSA by varying the parameters that controls its gravitational force. Then a new differential mutation operator is proposed to enhance performance of GSA by accelerating its convergence. The proposed algorithm, namely DMGSA, is evaluated using 15 well-known benchmark functions from the special session of CEC2013 with different characteristics including randomly shifted optimum, rotation and non-separability. The results obviously confirms the performance achieved from the proposed mutation operator outperforms that from the attempts of parameter tuning in the original GSA. Lastly, DMGSA is applied for optimizing a small-scale gene regulatory network. The result demonstrates that its performance is highly competitive and clearly surpasses original GSA.

A Modified Gravity p-Median Model for Optimizing Facility Locations

AbstractThe gravityp-median model is an important improvement to the widely-usedp-median model. However, there is still a debate on its validity in empirical applications. Previous studies even doubt the significance of the gravityp-median model. Using a case study of tertiary hospitals in Shenzhen, China, this study re-examines the difference between the gravityp-median model with thep-median model, by decomposing the difference between the two models into gravity rule and variant attraction. This study also proposes a modified gravityp-median model by incorporating a distance threshold. The empirical results support the validity of the gravityp-median model, and also reveal that only when the attractions of candidate facility locations are variable will the gravityp-median model lead to different results with thep-median model. The difference between the modified gravityp-median model and the gravityp-median model is also examined. Moreover, the impacts of the distance-decay parameter and distance threshold on solutions are investigated. Results indicate that a larger distance-decay parameter tends to result in a more dispersed distribution of optimal facilities and a smaller average travel time, and a smaller distance threshold can better promote the spatial equity of facilities. The proposed method can also be applied in studies of other types of facilities or in other areas.

Optimized gravitational-based data clustering algorithm

Gravitational clustering is a nature-inspired and heuristic-based technique. The performance of nature-inspired algorithms relies on the balance achieved between exploitation and exploration. A modification over a data clustering algorithm based on the universal gravity rule is proposed in this paper. Although gravitational clustering algorithm has a high exploration ability, it lacks a proper exploitation mechanism because of the impulsive velocity of agents that search the solution space, which leads to the huge step size of agent positions through iterations. This study proposes the following solutions to impose a balance between exploitation and exploration: (i) the dependence of the agent on velocity history is removed to avoid high velocity caused by accumulating previous velocities, and (ii) an initialization step of centroid positions is added using the variance and median initialization method with a predefined number of clusters. The initialization step eliminates the effects of random initialization and subrogates the exploration process. Experiments are conducted using 13 benchmark datasets from the UCI machine learning repository. In addition, the proposed algorithm is tested on two case studies using the electrical hotspots and cervical cell datasets. The performance of the proposed clustering algorithm is compared qualitatively and quantitatively with several state-of-the-art clustering algorithms. The obtained results indicate that the proposed clustering algorithm outperforms conventional techniques. Furthermore, the clusters obtained using the proposed algorithm are more homogeneous than those obtained using conventional techniques. The proposed algorithm quantitatively achieves better results than the other techniques in 9 out of 15 datasets in terms of accuracy, F-score, and purity.

Dynamic Competitive Supply Chain Network Design with Price Dependent Demand and Huff Utility Function

This paper develops a two-stage model to consider a franchise/franchisee environment in which supply chains are simultaneously entering the untapped market to produce either identical or highly substitutable products and give franchise to franchisees. Customer demand is elastic, price dependent and customer utility function is based on Huff gravity rule model. The supply chains, in the first stage, shape their networks and set the market prices based on dynamic games. The franchisees, in the second stage, specify their attractiveness levels and set the locations of their retailers in simultaneous games. Possibility theory was also applied to cope with uncertainty. Finally, we applied our model to a real world problem, discussed the results, conducted some sensitivity analyses, and gained some managerial insights.

Many to many hub and spoke location routing problem based on the gravity rule

Many to many hub and spoke location routing problem based on the gravity rule

Facility and hub location model based on gravity rule

This paper presents a model for “Domestic Facility and Hub Location” in transportation network. Number of hubs and facilities are unknown and the objective is to minimize the transportation cost, lost demand penalty and facility set up costs. Also the distribution of demand among domestic facilities and hubs adheres to gravity rule. To solve the problem, sequential and integrated approaches were undertaken. In the sequential approach, domestic facilities were allocated and hubs were selected afterwards. However the integrated approach deals with both concurrently, with a more sophisticated steps reaching to the solution. This is mainly because the demand itself is highly dependent to where the facilities are going to be located. Heuristic solutions were employed and was tested in a set of Turkish network and validated by CPLEX software in small cases.

Novel Heuristic Algorithm for Large-scale Complex Optimization

Research in finance and lots of other areas often encounter large-scale complex optimization problems that are hard to find solutions. Classic heuristic algorithms often have limitations from the objectives that they are trying to mimic, leading to drawbacks such as lacking memory-efficiency, trapping in local optimal solutions, unstable performances, etc. This work considers imitating market competition behavior (MCB) and develops a novel heuristic algorithm accordingly, which combines characteristics of searching-efficiency, memory-efficiency, conflict avoidance, recombination, mutation and elimination mechanism. In searching space, the MCB algorithm updates solution dots according to the inertia and gravity rule, avoids falling into local optimal solution by introducing new enterprises while ruling out of the old enterprises at each iteration, and recombines velocity vector to speed up solution searching efficiency. This algorithm is capable of solving large-scale complex optimization model of large input dimension, including Over Lapping Generation Models, and can be easily applied to solve for other complex financial models. As a sample case, MCB algorithm is applied to a hybrid investment optimization model on R&D, riskless and risky assets over a continuous time period.

A data clustering approach based on universal gravity rule

In this paper, a new robust data clustering algorithm inspired by Newtonian law of gravity is proposed. The proposed algorithm not only reduces the effects of noise and outliers but also, it is not sensible to the initial positions of the centroids. In the proposed method, data points and the cluster centroids are considered as fixed celestial objects and movable objects, respectively. The celestial objects apply a gravity force to the movable objects and change their positions in the feature space and therefore, the best positions of the cluster centroids are determined by employing the law of gravity. To evaluate the performance of the proposed algorithm, a comparative experimental study with some well-known clustering algorithms, using three visual datasets as well as several benchmark datasets from UCI, is performed. The experimental results confirm the effectiveness and the efficiency of the proposed clustering algorithm.

Gravitational Signature of Matter-Antimatter Interaction

The gravitational signature of antimatter has received growing interest during the past few decades. Much of the theoretical work in ordinary tensor gravity rules out any difference in the gravitational interaction of matter and antimatter. Fundamental principles and theoretical models describing the nature of matter and antimatter are reviewed. The implication of a probable repulsive field between matter and antimatter and its far reaching consequences on certain cosmic issues, such as the early phase of the Big Bang, the Hawking radiation, virtual particle production and annihilations, are discussed. Experiments designed to probe the gravitational signature of antimatter are reviewed, and a new space-borne experiment to probe the nature of matter-antimatter interactions is proposed.

The BLUE-points: three standardized points used in the BLUE-protocol for ultrasound assessment of the lung in acute respiratory failure

Sir,The BLUE-protocol is designed for immediate diagnosis ofacute respiratory failure, one of the most distressing set-tings for patients [1]. Physicians would appreciate stan-dardized areas of investigation. The BLUE-points respondto this quest. Of the seven principles of lung ultrasound [2],principle 2, indicating that the disorders are distributedalong an Earth–Sky axis, and principle 3, which recalls thatthe lung is the most voluminous organ, are used.Bedside use and absence of irradiation favor the use ofultrasound [3]. Studies have shown that most acute lungdisorders benefit from standardized locations. Clinicallyrelevant interstitial syndrome locates anteriorly [4]. Freepneumothoraces always locate anteriorly [5]. As free fluidsfollow gravity rules [6], all cases of free pleural fluideffusion, regardless its abundance locate at least posteriorlyabove the diaphragm [7]. Lung consolidation can locateeverywhere depending on the cause and extension, yieldingsensitivity lower than other disorders, 90% [8], yet itsposterior location above the diaphragm in critically illpatients with pneumonia is usual [4].We consider the hands of the patient (called BLUE-hands, roughly standard doctor’s hands size in standardadults; physicians with large or small hands would makethe adaptation once for all). The hands, applied as shown inFig. 1, define the anterior chest wall. This is of great helpin patients with major obesity, nipple ptosis, etc. Thisworks with patients of any size, neonate included. TheBLUE-hands allows to define five regions of interest. Theupper and lower BLUE-points, the phrenic line, defined inFig. 1, are anterior. The phrenic point is lateral, defined inFig. 2. The PLAPS-point, defined in Fig. 2 from the lowerBLUE-point, is posterior. The Carmen maneuver extendsthe location of the BLUE-points: by a slight back-and-forthmovement of the probe held longitudinally to the left andthe right, remaining at the same skin point and gently

The gravity multiple server location problem

Models for locating facilities and service providers to serve a set of demand points are proposed. The number of facilities is unknown, however, there is a given number of servers to be distributed among the facilities. Each facility acts as an M/M/k queuing system. The objective function is the minimization of the combined travel time and the waiting time at the facility for all customers. The distribution of demand among the facilities is governed by the gravity rule. Two models are proposed: a stationary one and an interactive one. In the stationary model it is assumed that customers do not consider the waiting time at the facility in their facility selection decision. In the interactive model we assume that customers know the expected waiting time at the facility and consider it in their facility selection decision. The interactive model is more complicated because the allocation of the demand among the facilities depends on the demand itself. The models are analyzed and three heuristic solution algorithms are proposed. The algorithms were tested on a set of problems with up to 1000 demand points and 20 servers.

Multiple Facilities Location in the Plane Using the Gravity Model

Two problems are considered in this article. Both problems seek the location of p facilities. The first problem is the p median where the total distance traveled by customers is minimized. The second problem focuses on equalizing demand across facilities by minimizing the variance of total demand attracted to each facility. These models are unique in that the gravity rule is used for the allocation of demand among facilities rather than assuming that each customer selects the closest facility. In addition, we also consider a multiobjective approach, which combines the two objectives. We propose heuristic solution procedures for the problem in the plane. Extensive computational results are presented.

Natural history of flowers and gravity

Many flowers have coevolved with their pollinator animals. Gravity has been one of selection pressure for the evolution of flowers. Gravity rules morphology and other features of flowers in many aspects. Pair matching between the flower and its specific pollinator is one of factors that determine the fitness of both sides. Evolution of flower morphology and its molecular basis are reviewed briefly. Anemophilous flowers are also under the influence of gravity. Shape and other features of entomophilous flowers have been highly diversed. Gravitropic response and its mechanism are summarized. Recent findings on gravitropism and phototropism of pistils and stamens are presented in this article.

Chasing the moon's shadow.

Chasing the moon's shadow.