Diversity: Cultural and biological

Early human populations utilized a wide range of biological resources in a tremendous diversity of environments. As a result, they possessed high levels of cultural diversity dependent on and supportive of high levels of biological diversity. This pattern changed drastically with technological innovations enabling certain human groups to break down territorial barriers and to usurp resources of other groups. The dominant groups have gone on to exhaust a whole range of resources, depleting both biological and cultural diversity. Traditions of resource conservation can, however, re-emerge when the dominant cultures spread over the entire area and the innovations diffuse to other human groups. This could change once again as genetically engineered organisms become an economically viable proposition with the accruing advantages concentrated in the hands of a few human groups: a further drastic reduction in biological and cultural diversity may ensue.

Cultural-Evolution of Ecological Prudence

Animals often behave in a profligate fashion and decimate the populations of plants and animals they depend upon. They may, however, evolve prudent behaviour under special conditions, namely when such prudence greatly enhances the success of populations that are not too prone to invasions by profligate individuals. Cultural evolution in human societies can also lead to the adoption of prudent practices under similar conditions. These are more likely to be realized in stable environments in which the human populations tend to grow close to the carrying capacity, when the human groups are closed, and when the technology is stagnant. These conditions probably prevailed in the hunter—gatherer societies of the tropics and subtropics, and led to the adoption of a number of socially imposed restraints on the use of plant and animal resources. Such practices were rationalized in the form of Nature-worship. The Indian caste society became so organized as to fulfill these conditions, and gave rise to two religions, Buddhism and Jainism, which emphasize compassion towards all forms of life. The pastoral nomads of the middle east, on the other hand, lived in an environment which militated against prudence, and these societies gave rise to religions like Christianity, which declared war on nature. As the ruling elite and state have grown in power, they have tried to wrest control of natural resources from the local communities. This has sometimes resulted in conservation and prudent use under guidance from the state, but has often led to conflicts with local populations to the detriment of prudent behaviour. Modern technological progress has also often removed the need for conservation, as when availability of coal permitted the deforestation of England. While modern scientific understanding has led to a better appreciation of the need for prudence, the prevailing social and economic conditions often militate against any implementation of the understanding, as is seen from the history of whaling. However, the imperative for survival of the poor from the Third-World countries may finally bring about conditions in which ecological prudence may once again come to dominate human cultures as it might once have done with stable societies of hunter—gatherers.

Collision avoidance between UAV clusters using swarm intelligence techniques

In this article, several basic swarming laws for Unmanned Aerial Vehicles (UAVs) are developed for both two-dimensional (2D) plane and three-dimensional (3D) space. Effects of these basic laws on the group behaviour of swarms of UAVs are studied. It is shown that when cohesion rule is applied an equilibrium condition is reached in which all the UAVs settle at the same altitude on a circle of constant radius. It is also proved analytically that this equilibrium condition is stable for all values of velocity and acceleration. A decentralised autonomous decision-making approach that achieves collision avoidance without any central authority is also proposed in this article. Algorithms are developed with the help of these swarming laws for two types of collision avoidance, Group-wise and Individual, in 2D plane and 3D space. Effect of various parameters are studied on both types of collision avoidance schemes through extensive simulations.

Swarm intelligence-based solver for parameter estimation of laboratory through-diffusion transport of contaminants

Theoretical approaches are of fundamental importance to predict the potential impact of waste disposal facilities on ground water contamination. Appropriate design parameters are generally estimated be fitting theoretical models to data gathered from field monitoring or laboratory experiments. Transient through-diffusion tests are generally conducted in the laboratory to estimate the mass transport parameters of the proposed barrier material. Thes parameters are usually estimated either by approximate eye-fitting calibration or by combining the solution of the direct problem with any available gradient-based techniques. In this work, an automated, gradient-free solver is developed to estimate the mass transport parameters of a transient through-diffusion model. The proposed inverse model uses a particle swarm optimization (PSO) algorithm that is based on the social behavior of animals searching for food sources. The finite difference numerical solution of the forward model is integrated with the PSO algorithm to solve the inverse problem of parameter estimation. The working principle of the new solver is demonstrated and mass transport parameters are estimated from laboratory through-diffusion experimental data. An inverse model based on the standard gradient-based technique is formulated to compare with the proposed solver. A detailed comparative study is carried out between conventional methods and the proposed solver. The present automated technique is found to be very efficient and robust. The mass transport parameters are obtained with great precision.

Application of artificial intelligence for the assessment of the status of power transformers

The Dissolved Gas Analysis (DGA) a non destructive test procedure, has been in vogue for a long time now, for assessing the status of power and related transformers in service. An early indication of likely internal faults that may exist in Transformers has been seen to be revealed, to a reasonable degree of accuracy by the DGA. The data acquisition and subsequent analysis needs an expert in the concerned area to accurately assess the condition of the equipment. Since the presence of the expert is not always guaranteed, it is incumbent on the part of the power utilities to requisition a well planned and reliable artificial expert system to replace, at least in part, an expert. This paper presents the application of Ordered Ant Mner (OAM) classifier for the prediction of involved fault. Secondly, the paper also attempts to estimate the remaining life of the power transformer as an extension to the elapsed life estimation method suggested in the literature.

Not in their genes: phenotypic flexibility, behavioural traditions and cultural evolution in wild bonnet macaques

Phenotypic flexibility, or the within-genotype, context-dependent, variation in behaviour expressed by single reproductively mature individuals during their lifetimes, often impart a selective advantage to organisms and profoundly influence their survival and reproduction. Another phenomenon apparently not under direct genetic control is behavioural inheritance whereby higher animals are able to acquire information from the behaviour of others by social learning, and, through their own modified behaviour, transmit such information between individuals and across generations. Behavioural information transfer of this nature thus represents another form of inheritance that operates in many animals in tandem with the more basic genetic system. This paper examines the impact that phenotypic flexibility, behavioural inheritance and socially transmitted cultural traditions may have in shaping the structure and dynamics of a primate society--that of the bonnet macaque (Macaca radiata), a primate species endemic to peninsular India. Three principal issues are considered: the role of phenotypic flexibility in shaping social behaviour, the occurrence of individual behavioural traits leading to the establishment of social traditions, and the appearance of cultural evolution amidst such social traditions. Although more prolonged observations are required, these initial findings suggest that phenotypic plasticity, behavioural inheritance and cultural traditions may be much more widespread among primates than have previously been assumed but may have escaped attention due to a preoccupation with genetic inheritance in zoological thinking.

Prediction of swelling pressure of soil using artificial intelligence techniques

The swelling pressure of soil depends upon various soil parameters such as mineralogy, clay content, Atterberg's limits, dry density, moisture content, initial degree of saturation, etc. along with structural and environmental factors. It is very difficult to model and analyze swelling pressure effectively taking all the above aspects into consideration. Various statistical/empirical methods have been attempted to predict the swelling pressure based on index properties of soil. In this paper, the computational intelligence techniques artificial neural network and support vector machine have been used to develop models based on the set of available experimental results to predict swelling pressure from the inputs; natural moisture content, dry density, liquid limit, plasticity index, and clay fraction. The generalization of the model to new set of data other than the training set of data is discussed which is required for successful application of a model. A detailed study of the relative performance of the computational intelligence techniques has been carried out based on different statistical performance criteria.

Prediction of swelling pressure of soil using artificial intelligence techniques

The swelling pressure of soil depends upon various soil parameters such as mineralogy, clay content, Atterberg's limits, dry density, moisture content, initial degree of saturation, etc. along with structural and environmental factors. It is very difficult to model and analyze swelling pressure effectively taking all the above aspects into consideration. Various statistical/empirical methods have been attempted to predict the swelling pressure based on index properties of soil. In this paper, the computational intelligence techniques artificial neural network and support vector machine have been used to develop models based on the set of available experimental results to predict swelling pressure from the inputs; natural moisture content, dry density, liquid limit, plasticity index, and clay fraction. The generalization of the model to new set of data other than the training set of data is discussed which is required for successful application of a model. A detailed study of the relative performance of the computational intelligence techniques has been carried out based on different statistical performance criteria.

A vector measure for the intelligence of a question-answering (Q-A) system

The problem of quantification of intelligence of humans, and of intelligent systems, has been a challenging and controversial topic. IQ tests have been traditionally used to quantify human intelligence based on results of test designed by psychologists. It is in general very difficult to quantify intelligence. In this paper the authors consider a simple question-answering (Q-A) system and use this to quantify intelligence. The authors quantify intelligence as a vector with three components. The components consist of a measure of knowledge in asking questions, effectiveness of questions asked, and correctness of deduction. The authors formalize these parameters and have conducted experiments on humans to measure these parameters

Artificial intelligence approaches to network management: recent advances and a survey

With increased number of new services and users being added to the communication network, management of such networks becomes crucial to provide assured quality of service. Finding skilled managers is often a problem. To alleviate this problem and also to provide assistance to the available network managers, network management has to be automated. Many attempts have been made in this direction and it is a promising area of interest to researchers in both academia and industry. In this paper, a review of the management complexities in present day networks and artificial intelligence approaches to network management are presented. Published by Elsevier Science B.V.

Swarm intelligence algorithms for integrated optimization of piezoelectric actuator and sensor placement and feedback gains

Swarm intelligence algorithms are applied for optimal control of flexible smart structures bonded with piezoelectric actuators and sensors. The optimal locations of actuators/sensors and feedback gain are obtained by maximizing the energy dissipated by the feedback control system. We provide a mathematical proof that this system is uncontrollable if the actuators and sensors are placed at the nodal points of the mode shapes. The optimal locations of actuators/sensors and feedback gain represent a constrained non-linear optimization problem. This problem is converted to an unconstrained optimization problem by using penalty functions. Two swarm intelligence algorithms, namely, Artificial bee colony (ABC) and glowworm swarm optimization (GSO) algorithms, are considered to obtain the optimal solution. In earlier published research, a cantilever beam with one and two collocated actuator(s)/sensor(s) was considered and the numerical results were obtained by using genetic algorithm and gradient based optimization methods. We consider the same problem and present the results obtained by using the swarm intelligence algorithms ABC and GSO. An extension of this cantilever beam problem with five collocated actuators/sensors is considered and the numerical results obtained by using the ABC and GSO algorithms are presented. The effect of increasing the number of design variables (locations of actuators and sensors and gain) on the optimization process is investigated. It is shown that the ABC and GSO algorithms are robust and are good choices for the optimization of smart structures.