The tectonic evolution of the Eastern Limassol Forest Complex, Cyprus

The Eastern Limassol Forest Complex (ELFC) lies at the southern margin of the Troodos ophiolite, Cyprus, and preserves a Penrose-type stratigraphy with a 4km-thick crustal sequence. The ELFC is separated from the main part of the Troodos Massif by an east-west trending fault zone, the Arakapas Fault Belt, which earlier studies suggest formed the northern wall of an oceanic transform fault. Transform-related structures are identifiable in the northern part of the ELFC, and volcaniclastic turbiditic sediments intercalated with lava flows attest to the existence of a bathymetric depression coincident with the fault zone. A southern boundary to the transform fault zone is recognised within the ELFC, with the abrupt disappearance of interlava sediments and E-W trending structures. Crust to the south of the boundary was generated at an 'Anti-Troodos' ridge axis. A width of c.5km is implied for the transform. The accretionary geometry of the ELFC has been extensively modified by postvolcanic tectonism. Sustained extension oblique to the trend of the transform has resulted in the reactivation of transform-related structures as normal faults, which have been rotated 'falling domino' style, together with the greater part of the axis sequence crust, above a decollement horizon located near to the petrological Moho. Extensional strain was preferentially accommodated in the transform-tectonised north of the ELFC. In the south, NW-striking normal faults are more steeply dipping, and block tilting is less extreme. Mesostructural data suggest that these normal faults have been reactivated as oblique dextral strike-slip faults and, with subsidiary NE-trending structures, are responsible for clockwise block rotations about steeply plunging axes. The timing of the deformation is constrained with respect to the overlying pelagic sediments, which suggest that the extension continued from the Turonian (i.e. almost immediately after ophiolite formation) to the late Campanian, and that the strike-slip reactivation occurred in late Campanian to early Maastrictian times. Palaeomagnetic studies have shown that Cyprus experienced a 90· anticlockwise rotation, which commenced in the Campanian-Maastrichtian interval, and it is argued that the late dextral strike-slip movements in the southern ELFC reflect deformation close to the margin of the rotating Cyprus microplate. The extensional reactivation of the transform in the Turonian-Campanian may correspond to an anticlockwise torque applied to the Troodos ocean floor prior to actual rotation. The rotation of Cyprus is thought to have been a consequence of the collision of the Arabian continental promontory to the east with an intra-oceanic subduction zone (above which Troodos was created) in the Upper Cretaceous

Combining biochemical network motifs within an ARN-agent control system.

The Artificial Reaction Network (ARN) is an Artificial Chemistry representation inspired by cell signaling networks. The ARN has previously been applied to the simulation of the chemotaxis pathway of Escherichia coli and to the control of limbed robots. In this paper we discuss the design of an ARN control system composed of a combination of network motifs found in actual biochemical networks. Using this control system we create multiple cell-like autonomous agents capable of coordinating all aspects of their behavior, recognizing environmental patterns and communicating with other agent's stigmergically. The agents are applied to simulate two phases of the life cycle of Dictyostelium discoideum: vegetative and aggregation phase including the transition. The results of the simulation show that the ARN is well suited for construction of biochemical regulatory networks. Furthermore, it is a powerful tool for modeling multi agent systems such as a population of amoebae or bacterial colony

Artificial reaction networks.

In this paper we present a novel method of simulating cellular intelligence, the Artificial Reaction Network (ARN). The ARN can be described as a modular S-System, with some properties in common with other Systems Biology and AI techniques, including Random Boolean Networks, Petri Nets, Artificial Biochemical Networks and Artificial Neural Networks. We validate the ARN against standard biological data, and successfully apply it to simulate cellular intelligence associated with the well-characterized cell signaling network of Escherichia coli chemotaxis. Finally, we explore the adaptability of the ARN, as a means to develop novel AI techniques, by successfully applying the simulated E. coli chemotaxis to a general optimization problem

Exploring aspects of cell intelligence with artificial reaction networks.

The Artificial Reaction Network (ARN) is a Cell Signalling Network inspired connectionist representation belonging to the branch of A-Life known as Artificial Chemistry. Its purpose is to represent chemical circuitry and to explore computational properties responsible for generating emergent high-level behaviour associated with cells. In this paper, the computational mechanisms involved in pattern recognition and spatio-temporal pattern generation are examined in robotic control tasks. The results show that the ARN has application in limbed robotic control and computational functionality in common with Artificial Neural Networks. Like spiking neural models, the ARN can combine pattern recognition and complex temporal control functionality in a single network, however it offers increased flexibility. Furthermore, the results illustrate parallels between emergent neural and cell intelligence

Artificial chemistry approach to exploring search spaces using artificial reaction network agents.

The Artificial Reaction Network (ARN) is a cell signaling network inspired representation belonging to the branch of A-Life known as Artificial Chemistry. It has properties in common with both AI and Systems Biology techniques including Artificial Neural Networks, Petri Nets, Random Boolean Networks and S-Systems. The ARN has been previously applied to control of limbed robots and simulation of biological signaling pathways. In this paper, multiple instances of independent distributed ARN controlled agents function to find the global minima within a set of simulated environments characterized by benchmark problems. The search behavior results from the internal ARN network, but is enhanced by collective activities and stigmergic interaction of the agents. The results show that the agents are able to find best fitness solutions in all problems, and compare well with results of cell inspired optimization algorithms. Such a system may have practical application in distributed or swarm robotics

Supporting better decisions across the nexus of water, energy and food through earth observation data:Case of the Zambezi basin

The water–energy–food (WEF) nexus has been promoted in recent years as an intersectional concept designed to improve planning and regulatory decision-making across the three sectors. The production and consumption of water, energy and food resources are inextricably linked across multiple spatial scales (from the global to the local), but a common feature is competition for land which through different land management practices mediates provisioning ecosystem services. The nexus perspective seeks to understand the interlinkages and use systems-based thinking to frame management options for the present and the future. It aims to highlight advantage and minimise damaging and unsustainable outcomes through informed decisions regarding trade-offs inclusive of economic, ecological and equity considerations. Operationalizing the WEF approach is difficult because of the lack of complete data, knowledge and observability – and the nature of the challenge also depends on the scale of the investigation. Transboundary river basins are particularly challenging because whilst the basin unit defines the hydrological system this is not necessarily coincident with flows of food and energy. There are multiple national jurisdictions and geopolitical relations to consider. Land use changes have a profound influence on hydrological, agricultural, energy provisioning and regulating ecosystem services. Future policy decisions in the water, energy and food sectors could have profound effects, with different demands for land and water resources, intensifying competition for these resources in the future. In this study, we used Google Earth Engine (GEE) to analyse the land cover changes in the Zambezi river basin (1.4 million km²) from 1992 to 2015 using the European Space Agency annual global land cover dataset. Early results indicate transformative processes are underway with significant shifts from tree cover to cropland, with a 4.6 % loss in tree cover and a 16 % gain in cropland during the study period. The changes were found to be occurring mainly in the eastern (Malawi and Mozambique) and southern (Zimbabwe and southern Zambia) parts of the basin. The area under urban land uses was found to have more than doubled during the study period gearing urban centres increasingly as the foci for resource consumption. These preliminary findings are the first step in understanding the spatial and temporal interlinkages of water, energy and food by providing reliable and consistent evidence spanning the local, regional, national and whole transboundary basin scale

Applications and design of cooperative multi-agent ARN-based systems.

The Artificial Reaction Network (ARN) is an Artificial Chemistry inspired by Cell Signalling Networks (CSNs). Its purpose is to represent chemical circuitry and to explore the computational properties responsible for generating emergent high-level behaviour. In previous work, the ARN was applied to the simulation of the chemotaxis pathway of E. coli and to the control of quadrupedal robotic gaits. In this paper, the design and application of ARN-based cell-like agents termed Cytobots are explored. Such agents provide a facility to explore the dynamics and emergent properties of multicellular systems. The Cytobot ARN is constructed by combining functional motifs found in real biochemical networks. By instantiating this ARN, multiple Cytobots are created, each of which is capable of recognizing environmental patterns, stigmergic communication with others and controlling its own trajectory. Applications in biological simulation and robotics are investigated by first applying the agents to model the life-cycle phases of the cellular slime mould D. discoideum and then to simulate an oil-spill clean-up operation. The results demonstrate that an ARN based approach provides a powerful tool for modelling multi-agent biological systems and also has application in swarm robotics

An Experimental Investigation of the Reinforcing and Extinguishing Effects of Implicit Rewards on Children's Handwriting

Two studies were conducted to investigate the effects of feedback of results, verbal praise, approval stamps and sweets as rewards for the correct letter-writing responses of typical elementary school children. The first study examined the effects of age and group-size upon the children's responses to variations in reward-administration procedures. Data were collected on the principal dependent variable of handwriting, comments and complaints were recorded, and a post-intervention questionnaire administered