'Nothing exists for one cause' : putting biology back into evolution

The opening phrase of the title is from Charles Darwin’s notebooks (Schweber 1977). It is a double reminder, firstly that mainstream evolutionary theory is not just about describing nature but is particularly looking for mechanisms or ‘causes’, and secondly, that there will usually be several causes affecting any particular outcome. The second part of the title is our concern at the almost universal rejection of the idea that biological mechanisms are sufficient for macroevolutionary changes, thus rejecting a cornerstone of Darwinian evolutionary theory. Our primary aim here is to consider ways of making it easier to develop and to test hypotheses about evolution. Formalizing hypotheses can help generate tests. In an absolute sense, some of the discussion by scientists about evolution is little better than the lack of reasoning used by those advocating intelligent design. Our discussion here is in a Popperian framework where science is defined by that area of study where it is possible, in principle, to find evidence against hypotheses – they are in principle falsifiable. However, with time, the boundaries of science keep expanding. In the past, some aspects of evolution were outside the current boundaries of falsifiable science, but increasingly new techniques and ideas are expanding the boundaries of science and it is appropriate to re-examine some topics. It often appears that over the last few decades there has been an increasingly strong assumption to look first (and only) for a physical cause. This decision is virtually never formally discussed, just an assumption is made that some physical factor ‘drives’ evolution. It is necessary to examine our assumptions much more carefully. What is meant by physical factors ‘driving’ evolution, or what is an ‘explosive radiation’. Our discussion focuses on two of the six mass extinctions, the fifth being events in the Late Cretaceous, and the sixth starting at least 50,000 years ago (and is ongoing). Cretaceous/Tertiary boundary; the rise of birds and mammals. We have had a long-term interest (Cooper and Penny 1997) in designing tests to help evaluate whether the processes of microevolution are sufficient to explain macroevolution. The real challenge is to formulate hypotheses in a testable way. For example the numbers of lineages of birds and mammals that survive from the Cretaceous to the present is one test. Our first estimate was 22 for birds, and current work is tending to increase this value. This still does not consider lineages that survived into the Tertiary, and then went extinct later. Our initial suggestion was probably too narrow in that it lumped four models from Penny and Phillips (2004) into one model. This reduction is too simplistic in that we need to know about survival and ecological and morphological divergences during the Late Cretaceous, and whether Crown groups of avian or mammalian orders may have existed back into the Cretaceous. More recently (Penny and Phillips 2004) we have formalized hypotheses about dinosaurs and pterosaurs, with the prediction that interactions between mammals (and groundfeeding birds) and dinosaurs would be most likely to affect the smallest dinosaurs, and similarly interactions between birds and pterosaurs would particularly affect the smaller pterosaurs. There is now evidence for both classes of interactions, with the smallest dinosaurs and pterosaurs declining first, as predicted. Thus, testable models are now possible. Mass extinction number six: human impacts. On a broad scale, there is a good correlation between time of human arrival, and increased extinctions (Hurles et al. 2003; Martin 2005; Figure 1). However, it is necessary to distinguish different time scales (Penny 2005) and on a finer scale there are still large numbers of possibilities. In Hurles et al. (2003) we mentioned habitat modification (including the use of Geogenes III July 2006 31 fire), introduced plants and animals (including kiore) in addition to direct predation (the ‘overkill’ hypothesis). We need also to consider prey switching that occurs in early human societies, as evidenced by the results of Wragg (1995) on the middens of different ages on Henderson Island in the Pitcairn group. In addition, the presence of human-wary or humanadapted animals will affect the distribution in the subfossil record. A better understanding of human impacts world-wide, in conjunction with pre-scientific knowledge will make it easier to discuss the issues by removing ‘blame’. While continued spontaneous generation was accepted universally, there was the expectation that animals continued to reappear. New Zealand is one of the very best locations in the world to study many of these issues. Apart from the marine fossil record, some human impact events are extremely recent and the remains less disrupted by time.

Providing query support to leverage BIM for construction

Building information modeling (BIM) is an emerging technology and process that provides rich and intelligent design information models of a facility, enabling enhanced communication, coordination, analysis, and quality control throughout all phases of a building project. Although there are many documented benefits of BIM for construction, identifying essential construction-specific information out of a BIM in an efficient and meaningful way is still a challenging task. This paper presents a framework that combines feature-based modeling and query processing to leverage BIM for construction. The feature-based modeling representation implemented enriches a BIM by representing construction-specific design features relevant to different construction management (CM) functions. The query processing implemented allows for increased flexibility to specify queries and rapidly generate the desired view from a given BIM according to the varied requirements of a specific practitioner or domain. Central to the framework is the formalization of construction domain knowledge in the form of a feature ontology and query specifications. The implementation of our framework enables the automatic extraction and querying of a wide-range of design conditions that are relevant to construction practitioners. The validation studies conducted demonstrate that our approach is significantly more effective than existing solutions. The research described in this paper has the potential to improve the efficiency and effectiveness of decision-making processes in different CM functions.

Design optimisation using advanced artificial intelligent system coupled to hybrid-game strategies

One of the main aims in artificial intelligent system is to develop robust and efficient optimisation methods for Multi-Objective (MO) and Multidisciplinary Design (MDO) design problems. The paper investigates two different optimisation techniques for multi-objective design optimisation problems. The first optimisation method is a Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The second method combines the concepts of Nash-equilibrium and Pareto optimality with Multi-Objective Evolutionary Algorithms (MOEAs) which is denoted as Hybrid-Game. Numerical results from the two approaches are compared in terms of the quality of model and computational expense. The benefit of using the distributed hybrid game methodology for multi-objective design problems is demonstrated.

A new and efficient intelligent collaboration scheme for fashion design

Technology-mediated collaboration process has been extensively studied for over a decade. Most applications with collaboration concepts reported in the literature focus on enhancing efficiency and effectiveness of the decision-making processes in objective and well-structured workflows. However, relatively few previous studies have investigated the applications of collaboration schemes to problems with subjective and unstructured nature. In this paper, we explore a new intelligent collaboration scheme for fashion design which, by nature, relies heavily on human judgment and creativity. Techniques such as multicriteria decision making, fuzzy logic, and artificial neural network (ANN) models are employed. Industrial data sets are used for the analysis. Our experimental results suggest that the proposed scheme exhibits significant improvement over the traditional method in terms of the time–cost effectiveness, and a company interview with design professionals has confirmed its effectiveness and significance.

Fusion of in-vehicle sensor data to develop Intelligent Driver Training System (IDTS)

The over represented number of novice drivers involved in crashes is alarming. Driver training is one of the interventions aimed at mitigating the number of crashes that involve young drivers. To our knowledge, Advanced Driver Assistance Systems (ADAS) have never been comprehensively used in designing an intelligent driver training system. Currently, there is a need to develop and evaluate ADAS that could assess driving competencies. The aim is to develop an unsupervised system called Intelligent Driver Training System (IDTS) that analyzes crash risks in a given driving situation. In order to design a comprehensive IDTS, data is collected from the Driver, Vehicle and Environment (DVE), synchronized and analyzed. The first implementation phase of this intelligent driver training system deals with synchronizing multiple variables acquired from DVE. RTMaps is used to collect and synchronize data like GPS, vehicle dynamics and driver head movement. After the data synchronization, maneuvers are segmented out as right turn, left turn and overtake. Each maneuver is composed of several individual tasks that are necessary to be performed in a sequential manner. This paper focuses on turn maneuvers. Some of the tasks required in the analysis of ‘turn’ maneuver are: detect the start and end of the turn, detect the indicator status change, check if the indicator was turned on within a safe distance and check the lane keeping during the turn maneuver. This paper proposes a fusion and analysis of heterogeneous data, mainly involved in driving, to determine the risk factor of particular maneuvers within the drive. It also explains the segmentation and risk analysis of the turn maneuver in a drive.

Towards an intelligent learning system for the natural born cyborg

We propose to design a Custom Learning System that responds to the unique needs and potentials of individual students, regardless of their location, abilities, attitudes, and circumstances. This project is intentionally provocative and future-looking but it is not unrealistic or unfeasible. We propose that by combining complex learning databases with a learner’s personal data, we could provide all students with a personal, customizable, and flexible education. This paper presents the initial research undertaken for this project of which the main challenges were to broadly map the complex web of data available, to identify what logic models are required to make the data meaningful for learning, and to translate this knowledge into simple and easy-to-use interfaces. The ultimate outcome of this research will be a series of candidate user interfaces and a broad system logic model for a new smart system for personalized learning. This project is student-centered, not techno-centric, aiming to deliver innovative solutions for learners and schools. It is deliberately future-looking, allowing us to ask questions that take us beyond the limitations of today to motivate new demands on technology.

Evolutionary game design

Abstract—It is easy to create new combinatorial games but more difficult to predict those that will interest human players. We examine the concept of game quality, its automated measurement through self-play simulations, and its use in the evolutionary search for new high-quality games. A general game system called Ludi is described and experiments conducted to test its ability to synthesize and evaluate new games. Results demonstrate the validity of the approach through the automated creation of novel, interesting, and publishable games. Index Terms—Aesthetics, artificial intelligence (AI), combinatorial game, evolutionary search, game design.

Construction, application and validation of selection evaluation model (SEM) for intelligent HVAC control system

Design teams are confronted with the quandary of choosing apposite building control systems to suit the needs of particular intelligent building projects, due to the availability of innumerable ‘intelligent’ building products and a dearth of inclusive evaluation tools. This paper is organised to develop a model for facilitating the selection evaluation for intelligent HVAC control systems for commercial intelligent buildings. To achieve these objectives, systematic research activities have been conducted to first develop, test and refine the general conceptual model using consecutive surveys; then, to convert the developed conceptual framework into a practical model; and, finally, to evaluate the effectiveness of the model by means of expert validation. The results of the surveys are that ‘total energy use’ is perceived as the top selection criterion, followed by the‘system reliability and stability’, ‘operating and maintenance costs’, and ‘control of indoor humidity and temperature’. This research not only presents a systematic and structured approach to evaluate candidate intelligent HVAC control system against the critical selection criteria (CSC), but it also suggests a benchmark for the selection of one control system candidate against another.

An intelligent data mining technique for product quality improvement

Advances in data mining have provided techniques for automatically discovering underlying knowledge and extracting useful information from large volumes of data. Data mining offers tools for quick discovery of relationships, patterns and knowledge in large complex databases. Application of data mining to manufacturing is relatively limited mainly because of complexity of manufacturing data. Growing self organizing map (GSOM) algorithm has been proven to be an efficient algorithm to analyze unsupervised DNA data. However, it produced unsatisfactory clustering when used on some large manufacturing data. In this paper a data mining methodology has been proposed using a GSOM tool which was developed using a modified GSOM algorithm. The proposed method is used to generate clusters for good and faulty products from a manufacturing dataset. The clustering quality (CQ) measure proposed in the paper is used to evaluate the performance of the cluster maps. The paper also proposed an automatic identification of variables to find the most probable causative factor(s) that discriminate between good and faulty product by quickly examining the historical manufacturing data. The proposed method offers the manufacturers to smoothen the production flow and improve the quality of the products. Simulation results on small and large manufacturing data show the effectiveness of the proposed method.

Autonomous underwater vehicle trajectory design coupled with predictive ocean models : a case study

Data collection using Autonomous Underwater Vehicles (AUVs) is increasing in importance within the oceano- graphic research community. Contrary to traditional moored or static platforms, mobile sensors require intelligent planning strategies to manoeuvre through the ocean. However, the ability to navigate to high-value locations and collect data with specific scientific merit is worth the planning efforts. In this study, we examine the use of ocean model predictions to determine the locations to be visited by an AUV, and aid in planning the trajectory that the vehicle executes during the sampling mission. The objectives are: a) to provide near-real time, in situ measurements to a large-scale ocean model to increase the skill of future predictions, and b) to utilize ocean model predictions as a component in an end-to-end autonomous prediction and tasking system for aquatic, mobile sensor networks. We present an algorithm designed to generate paths for AUVs to track a dynamically evolving ocean feature utilizing ocean model predictions. This builds on previous work in this area by incorporating the predicted current velocities into the path planning to assist in solving the 3-D motion planning problem of steering an AUV between two selected locations. We present simulation results for tracking a fresh water plume by use of our algorithm. Additionally, we present experimental results from field trials that test the skill of the model used as well as the incorporation of the model predictions into an AUV trajectory planner. These results indicate a modest, but measurable, improvement in surfacing error when the model predictions are incorporated into the planner.