Vervet monkeys use paths consistent with context-specific spatial movement heuristics.

We report the discovery and sequence-based molecular characterization of a novel virus, lanama virus (LNMV), in blood samples obtained from two wild vervet monkeys (Chlorocebus pygerythrus), sampled near Lake Nabugabo, Masaka District, Uganda. Sequencing of the complete viral genomes and subsequent phylogenetic analysis identified LNMV as a distinct member of species Kunsagivirus C, in the undercharacterized picornavirid genus Kunsagivirus.

An important goal in foraging ecology is to determine how biotic and abiotic variables impact the foraging decisions of wild animals and how they move throughout their multidimensional landscape. However, the interaction of food quality and feeding competition on foraging decisions is largely unknown. Here we examine the importance of food quality in a patch on the foraging decisions of wild vervet monkeys (Chlorocebus pygerythrus) at Lake Nabugabo, Uganda using a multidestination platform array. The overall nutritional composition of the vervet diet was assessed and found to be low in sodium and lipids, thus we conducted a series of experimental manipulations in which the array was varied in salt and oil content. Although vervets prioritized platforms containing key nutrients (i.e., sodium and lipids) overall, we found that solitary vervets prioritized nutrient-dense platforms more strongly than competing vervets. This finding was opposite to those in a similar experiment that manipulated food site quantity, suggesting that large, salient rewards may be worth competing over but slight differences in nutritional density may be only chosen when there are no potentially negative social consequences (i.e., aggression received). We also found that vervets chose platforms baited with oil-only, and oil combined with salt, but not salt-only, suggesting that energy was an important factor in food choice. Our findings demonstrate that when wild vervets detect differences in feeding patches that reflect nutritional composition, they factor these differences into their navigational and foraging decisions. In addition, our findings suggest that these nutritional differences may be considered alongside social variables, ultimately leading to the complex strategies we observed in this study.

Discrete Spider Monkey Optimization for Travelling Salesman Problem

This article reviews how simple heuristics – ‘rules of thumb’ – have guided human adaptation and the evolution of complex cultures. First, we argue that rules of thumb have been important catalysts for the evolution of human knowledge systems in the Holocene past. Through a variety of examples and case studies, we discuss how human cultures have used simple heuristics in domains as diverse as foraging, agriculture, social learning, moral and legal judgement and technological development. We emphasise how rules of thumb are convenient units for cultural transmission, and how they can facilitate efficient decision making by making use of recurrent environmental features. Second, we caution that as Anthropocene environments rapidly change, many traditional heuristic problem-solving strategies will face challenges due to cultural evolutionary mismatch. Old rules may not function in new environments, creating potential challenges for traditional (ecological) knowledge.

BACKGROUNDMany of the reproductive decisions that humans make happen without much planning or forethought, arising instead through the use of simple choice rules or heuristics that involve relatively little information and processing. Nonetheless, these heuristic-guided decisions are typically beneficial, owing to humans' ecological rationality - the evolved fit between our constrained decision mechanisms and the adaptive problems we face.OBJECTIVEThis paper reviews research on the ecological rationality of human decision making in the domain of reproduction, showing how fertility-related decisions are commonly made using various simple heuristics matched to the structure of the environment in which they are applied, rather than being made with information-hungry mechanisms based on optimization or rational economic choice.METHODSFirst, heuristics for sequential mate search are covered; these heuristics determine when to stop the process of mate search by deciding that a good-enough mate who is also mutually interested has been found, using a process of aspiration-level setting and assessing. These models are tested via computer simulation and comparison to demographic age-at-first-marriage data. Next, a heuristic process of feature-based mate comparison and choice is discussed, in which mate choices are determined by a simple process of feature-matching with relaxing standards over time. Parental investment heuristics used to divide resources among offspring are summarized. Finally, methods for testing the use of such mate choice heuristics in a specific population over time are then described.1. IntroductionHumans make a variety of reproductive decisions throughout their lives, with the most important including who to choose as a mate, when to have offspring with that mate, and how to raise the children that result. Some of these decisions are consciously planned with great deliberation; others (probably the majority) may come about without deep thought, guided instead by simple strategies or rules of thumb and shaped by external environmental factors. And yet these common, less planned, more intuitive decisions often work out well in terms of reproductive success, leading to healthy members of the next generation. This happens despite the great uncertainty that may surround determining who will be a good mate or what is an appropriate way to provision one's children, and without needing to gather a lot of information about the possible courses of action before choosing a mate. In this paper, we outline some of the ways that simple heuristics can be used in making reproductive decisions, and how they can be studied. We begin with an overview of the perspective of ecological rationality within which this research is situated, before proceeding to cover investigations of heuristic rules for mate search and choice, parental provisioning of individual offspring, and decisions related to fertility.This research program on simple heuristics and their effectiveness in appropriate settings represents a new approach to questions in demographics. First, it builds a theoretically motivated, psychologically grounded framework for predicting and understanding population-level patterns of fertility-related behavior, based on the individual-level decision mechanisms that people actually use. Rather than the economists' vision of human decision making employing all of the available information, fully known preferences, and fully processed implications of both, simple heuristics are built on realistic assumptions of the limited information, time, and thinking that people are actually able to bring to bear on most of their choices. Second, this research program uses simulation models of these simple decision heuristics, in conjunction with empirical data from experiments and field observations, to test the effectiveness of the heuristics in different situations and their implications at the population level. …

Intersexual dominance relations are important for female mammals, because of their consequences for accessing food and for the degree of sexual control females experience from males. Female mammals are usually considered to rank below males in the dominance hierarchy, because of their typical physical inferiority. Yet, in some groups or species, females are nonetheless dominant over some males (partial female dominance). Intersexual dominance, therefore, also depends on traits other than sexual dimorphism, such as social support, social exchange, group adult sex-ratio, and the widespread self-reinforcing effects of winning and losing fights, the “winner-loser effect.” The importance of sex-ratio and the winner-loser effect remains poorly understood. A theoretical model, DomWorld, predicts that in groups with a higher proportion of males, females are dominant over more males when aggression is fierce (not mild). The model is based on a small number of general processes in mammals, such as grouping, aggression, the winner-loser effect, the initially greater fighting capacity of males than females, and sex ratio. We expect its predictions to be general and suggest they be examined in a great number of species and taxa. Here, we test these predictions in four groups of wild vervet monkeys (Chlorocebus pygerythrus) in Mawana game reserve in Africa, using 7 years of data. We confirm that a higher proportion of males in the group is associated with greater dominance of females over males; a result that remains when combining these data with those of two other sites (Amboseli and Samara). We additionally confirm that in groups with a higher fraction of males there is a relatively higher (a) proportion of fights of males with other males, and (b) proportion of fights won by females against males from the fights of females with any adults. We reject alternative hypotheses that more dominance of females over males could be attributed to females receiving more coalitions from males, or females receiving lowered male aggression in exchange for sexual access (the docile male hypothesis). We conclude that female dominance relative to males is dynamic and that future empirical studies of inter-sexual dominance will benefit by considering the adult sex-ratio of groups.

Two kinds of sensors will be discussed in some sort of wireless sensor networks (WSNs). One is named a normal sensor (called A_nodes) with fixed initial energy, which can get perception data from the surrounding environment and have functions of storage and forwarding. The other is named relay sensor (called B_node) with sufficient energy, which only can store data and forward data. Cluster heads (called A_heads) are chosen among A_nodes by probability mode to create clusters first. Local adjustments would be done inter-cluster. Then the sink is selected as the root and a backbone shortest path tree with hops limited is built dynamically from A_heads, B_Nodes and sink. Inside the backbone shortest path aggregation tree, two steps are done. The first step, select some B_node as cluster head (denoted as B_node) and make local adjustment intra-clusters if it is possible. The second step, adjust the shortest path aggregation tree. By using both particle swarm optimization (PSO) and ant colony optimization (ACO), a traveling salesman problem (TSP) cycle with shorter path length is obtained (this method is noted as TSP_PSO_ACO). This TSP cycle consists of all nodes in the aggregation backbone tree. Along this cycle, energy is offered to nodes except sink. The above strategy would be discussed for its feasibility and time complexity. The experimental results imply that the energy consumption is reduced by using local adjustment. The relative failure rate of nodes can be reduced in the later stage of WSN survival. Offering energy to a small number of nodes several times has no obvious impact on the overall energy consumption and survival time of WSN. The case with some small-sized obstacles in WSN area is also discussed. There are little affections on TSP cycles both from results on theoretical analysis and simulation if there are some obstacles with small size in WSN area.

Critical infrastructure protection has a significant role in the modern world. The critical infrastructure can be found in many fields, but the roads and road infrastructure surely are one of the very important factors in this domain. This paper targets the issue of utilization of Unmanned Aerial Vehicles (UAVs) for road infrastructure monitoring. The contribution of this paper is the methodology proposed for UAV path planning in urban and suburban scenarios. The purpose of UAVs is the city road condition monitoring, and the proposed methodology tends to optimize this process by usage of a minimal number of UAVs to monitor a maximal number of locations, by randomly chosen locations, and dynamically calculated paths. The methodology uses Traveling salesman Problem (TSP) with Genetic Algorithm (GA) support. The TSP solution gives the shortest direct path between randomly selected locations. To enable the following of the road TSP is expanded with Dijkstra’s shortest path algorithm giving, as a result, the calculated path which follows the city roads. The addition of Dijkstra’s shortest path algorithm does not significantly increase the length of the path, comparing to TSP calculated path.KeywordsUAV road infrastructure monitoringUAV critical infrastructure monitoringUAV path planningTravelling salesman problemDijkstra’s shortest path algorithm

¿Existe la enfermedad de Alzheimer en todos los primates? Patología Alzheimer en primates no humanos y sus implicaciones fisiopatológicas (II)

With the development of machine learning in various fields, it can also be applied to combinatorial optimization problems, automatically discovering generic and fast heuristic algorithms based on training data, and requires fewer theoretical and empirical knowledge. Pointer network improves the attention mechanism, instead of allocating different attention to hidden states of encoder to generate context vectors, using attention as a pointer to select an element of the input sequence at every step of decoding, which solves the problem of variable dictionary size of the output sequence. Pointer net (Ptr-Net) applied to three combinatorial optimization problems, convex hull, Delaunay triangulation, and traveling salesman problem (TSP), obtains good approximate solutions. Point matching is also a special kind of combinatorial optimization problems that is to obtain the optimal corresponding references, which can be modeled by Ptr-Net. However, Ptr-Net can’t be used to solve point matching problem because it doesn’t take full advantage of the correspondences between the two point sets. We propose multi-pointer network, which draws the idea from multi-label classification, to address this limitation by pointing out a set of input elements. These applications are all based on supervised learning to approximate expected known solutions. However, high-quality labeled data is often expensive, unreliable, or simply unavailable and may be infeasible for new problem statements, making supervised learning being unpractical. Reinforcement learning, as another research hotspot in the field of machine learning, does not require labeled sample data. It interacts with the environment through trial-and-error mechanism and focuses more on learning problem-solving strategies. We introduce a framework to tackle combinatorial optimization problems using neural networks and reinforcement learning, focusing on the traveling salesman problem. We also introduce a framework, a unique combination of reinforcement learning and graph embedding network, to solve graph optimization problems, focusing on maximum cut (MAXCUT) and minimum vertex cover (MVC) problems.

Hormonal correlates of male dominance rank, age, and genital colouration in vervet monkey (Chlorocebus pygerythrus)

Layered manufacturing in rapid prototyping is to fabricate prototype by using a laser beam to trace the cross-sectional contours of a product layer by layer. Such cross-sections of geometrical objects differ by layers and generally have more than one continuous contour in each layer. In an attempt to facilitate an efficient approach for path planning, the problem is simplified by approximating each of the continuous contours with its minimum circumscribed circle. The tool path planning for traversing all the contours in the same cross-section can then be simplified as the path planning of circles. With combing the algorithm of traveling salesman problem (TSP) and the shortest traversal path function of three circles, an odd-even circle adjusting algorithm is proposed in this paper for deriving the shortest traversal path of n circles. When the traversing order of the n circles is obtained by the preprocessing TSP algorithm, the shortest traversal path of n circles can be derived in linear time. It is superior to that of the random selection method. Experiments are carried out for arbitrary n circles to demonstrate the computational advantage of the proposed method.

For the problem of multi-point inspection of steel pylons by robots for very large bridges, the input inspection path points are abstracted as nodes and the weight of each path point is calculated according to the actual situation of the project, so as to establish a weighted undirected graph of the movement of magnetic adsorption robots between each point. The original problem can then be transformed into the problem of finding the shortest possible path (the minimum cost) for each node in a given group, visiting each node once and returning to the origin, namely the traveling salesman problem (TSP). In this study, a path optimization method for magnetic adsorption robots for steel pylons based on an improved ant colony optimization (ACO) algorithm is proposed, and the Euclidean TSP is solved using the improved ACO approach (IACO). Finally, the simulation results demonstrate that IACO has advantages in convergence speed and stability as compared to conventional ACO, which proves the validity of the proposed method.

A probable new member of the herpes virus group caused an outbreak of fatal infections in captive vervet monkeys. The pathological findings and some of the properties of the virus are described.

To compare longitudinal weight gain in captive and wild juvenile vervet monkeys and conduct an empirical assessment of different mechanistic growth models. Weights were collected from two groups of captive monkeys and two consecutive cohorts of wild monkeys until the end of the juvenile period (~800 days). The captive groups were each fed different diets, while the wild groups experienced different ecological conditions. Three different growth curve models were compared. By 800 days, the wild juveniles were lighter, with a slower maximum growth rate, and reached asymptote earlier than their captive counterparts. There were overall differences in weight and growth rate across the two wild cohorts. This corresponded to differences in resource availability. There was considerable overlap in growth rate and predicted adult weight of male and females in the first, but not the second, wild cohort. Maternal parity was not influential. While the von Bertalanffy curve provided the best fit to the data sets modeled together, the Logistic curve best described growth in the wild cohorts when considered separately. The growth curves of the two captive cohorts are likely to lie near the maximum attainable by juvenile vervets. It may be helpful to include deviations from these rates when assessing the performance of wild vervet monkeys. The comparison of wild and captive juveniles confirmed the value of comparing different growth curve models, and an appreciation that the best models may well differ for different populations. Choice of mechanistic growth model can, therefore, be empirically justified, rather than theoretically predetermined.