Using learning networks to understand complex systems: a case study of biological, geophysical and social research in the Amazon

Developing high-quality scientific research will be most effective if research communities with diverse skills and interests are able to share information and knowledge, are aware of the major challenges across disciplines, and can exploit economies of scale to provide robust answers and better inform policy. We evaluate opportunities and challenges facing the development of a more interactive research environment by developing an interdisciplinary synthesis of research on a single geographic region. We focus on the Amazon as it is of enormous regional and global environmental importance and faces a highly uncertain future. To take stock of existing knowledge and provide a framework for analysis we present a set of mini-reviews from fourteen different areas of research, encompassing taxonomy, biodiversity, biogeography, vegetation dynamics, landscape ecology, earth-atmosphere interactions, ecosystem processes, fire, deforestation dynamics, hydrology, hunting, conservation planning, livelihoods, and payments for ecosystem services. Each review highlights the current state of knowledge and identifies research priorities, including major challenges and opportunities. We show that while substantial progress is being made across many areas of scientific research, our understanding of specific issues is often dependent on knowledge from other disciplines. Accelerating the acquisition of reliable and contextualized knowledge about the fate of complex pristine and modified ecosystems is partly dependent on our ability to exploit economies of scale in shared resources and technical expertise, recognise and make explicit interconnections and feedbacks among sub-disciplines, increase the temporal and spatial scale of existing studies, and improve the dissemination of scientific findings to policy makers and society at large. Enhancing interaction among research efforts is vital if we are to make the most of limited funds and overcome the challenges posed by addressing large-scale interdisciplinary questions. Bringing together a diverse scientific community with a single geographic focus can help increase awareness of research questions both within and among disciplines, and reveal the opportunities that may exist for advancing acquisition of reliable knowledge. This approach could be useful for a variety of globally important scientific questions.

Multiple haptic targets for motion-impaired computer users

Although a number of studies have reported that force feedback gravity wells can improve performance in "point-and-click" tasks, there have been few studies addressing issues surrounding the use of gravity wells for multiple on-screen targets. This paper investigates the performance of users, both with and without motion-impairments, in a "point-and-click" task when an undesired haptic distractor is present. The importance of distractor location is studied explicitly. Results showed that gravity wells can still improve times and error rates, even on occasions when the cursor is pulled into a distractor. The greatest improvement is seen for the most impaired users. In addition to traditional measures such as time and errors, performance is studied in terms of measures of cursor movement along a path. Two cursor measures, angular distribution and temporal components, are proposed and their ability to explain performance differences is explored.

TIGGE: comparison of the prediction of Southern Hemisphere extratropical cyclones by different ensemble prediction systems.

The prediction of Northern Hemisphere (NH) extratropical cyclones by nine different ensemble prediction systems(EPSs), archived as part of The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE), has recently been explored using a cyclone tracking approach. This paper provides a continuation of this work, extending the analysis to the Southern Hemisphere (SH). While the EPSs have larger error in all cyclone properties in the SH, the relative performance of the different EPSs remains broadly consistent between the two hemispheres. Some interesting differences are also shown. The Chinese Meteorological Administration (CMA) EPS has a significantly lower level of performance in the SH compared to the NH. Previous NH results showed that the Centro de Previsao de Tempo e Estudos Climaticos (CPTEC) EPS underpredicts cyclone intensity. The results of this current study show that this bias is significantly larger in the SH. The CPTEC EPS also has very little spread in both hemispheres. As with the NH results, cyclone propagation speed is underpredicted by all the EPSs in the SH. To investigate this further, the bias was also computed for theECMWFhigh-resolution deterministic forecast. The bias was significantly smaller than the lower resolution ECMWF EPS.

Integrating augmented reality with home systems

Augmented Reality systems overlay computer generated information onto a user's natural senses. Where this additional information is visual, the information is overlaid on the user's natural visual field of view through a head mounted (or “head-up”) display device. Integrated Home Systems provides a network that links every electrical device in the home which provides to a user both control and data transparency across the network.

B-spline neural networks based PID controller for Hammerstein systems

A new PID tuning and controller approach is introduced for Hammerstein systems based on input/output data. A B-spline neural network is used to model the nonlinear static function in the Hammerstein system. The control signal is composed of a PID controller together with a correction term. In order to update the control signal, the multistep ahead predictions of the Hammerstein system based on the B-spline neural networks and the associated Jacobians matrix are calculated using the De Boor algorithms including both the functional and derivative recursions. A numerical example is utilized to demonstrate the efficacy of the proposed approaches.

Analyzing the persuasion context of the persuasive systems design model with the 3D-RAB model

Research into design methodology is one of the most challenging issues in the field of persuasive technology. However, the introduction of the Persuasive Systems Design model, and the consideration of the 3-Dimensional Re-lationship between Attitude and Behavior, offer to make persuasive technolo-gies more practically viable. In this paper we demonstrate how the 3-Dimensional Relationship between Attitude and Behavior guides the analysis of the persuasion context in the Persuasive System Design model. As a result, we propose a modification of the persuasion context and assert that the technology should be analyzed as part of strategy instead of event.

Steering without Circe : attending to reinforcing loops in social systems

Relating system dynamics to the broad systems movement, the key notion is that reinforcing loops deserve no less attention than balancing loops. Three specific propositions follow. First, since reinforcing loops arise in surprising places, investigations of complex systems must consider their possible existence and potential impact. Second, because the strength of reinforcing loops can be misinferred - we include an example from the field of servomechanisms - computer simulation can be essential. Be it project management, corporate growth or inventory oscillation, simulation helps to assess consequences of reinforcing loops and options for interventions. Third, in social systems the consequences of reinforcing loops are not inevitable. Examples concerning globalization illustrate how difficult it might be to challenge such assumptions. However, system dynamics and ideas from contemporary social theory help to show that even the most complex social systems are, in principle, subject to human influence. In conclusion, by employing these ideas, by attending to reinforcing as well as balancing loops, system dynamics work can improve the understanding of social systems and illuminate our choices when attempting to steer them.

A conceptual framework for aligning business and information systems from organisational learning perspective

The three decades of on-going executives’ concerns of how to achieve successful alignment between business and information technology shows the complexity of such a vital process. Most of the challenges of alignment are related to knowledge and organisational change and several researchers have introduced a number of mechanisms to address some of these challenges. However, these mechanisms pay less attention to multi-level effects, which results in a limited un-derstanding of alignment across levels. Therefore, we reviewed these challenges from a multi-level learning perspective and found that business and IT alignment is related to the balance of exploitation and exploration strategies with the intellec-tual content of individual, group and organisational levels.

An overview of interactive visual data mining techniques for knowledge discovery

n the past decade, the analysis of data has faced the challenge of dealing with very large and complex datasets and the real-time generation of data. Technologies to store and access these complex and large datasets are in place. However, robust and scalable analysis technologies are needed to extract meaningful information from these datasets. The research field of Information Visualization and Visual Data Analytics addresses this need. Information visualization and data mining are often used complementary to each other. Their common goal is the extraction of meaningful information from complex and possibly large data. However, though data mining focuses on the usage of silicon hardware, visualization techniques also aim to access the powerful image-processing capabilities of the human brain. This article highlights the research on data visualization and visual analytics techniques. Furthermore, we highlight existing visual analytics techniques, systems, and applications including a perspective on the field from the chemical process industry.

Effects of target expansion on selection performance in older computer users

Point and click interactions using a mouse are an integral part of computer use for current desktop systems. Compared with younger users though, older adults experience greater difficulties performing cursor positioning tasks, and this can present limitations to using a computer easily and effectively. Target expansion is a technique for improving pointing performance, where the target dynamically grows as the cursor approaches. This has the advantage that targets conserve screen real estate in their unexpanded state, yet can still provide the benefits of a larger area to click on. This paper presents two studies of target expansion with older and younger participants, involving multidirectional point-select tasks with a computer mouse. Study 1 compares static versus expanding targets, and Study 2 compares static targets with three alternative techniques for expansion. Results show that expansion can improve times by up to 14%, and reduce error rates by up to 50%. Additionally, expanding targets are beneficial even when the expansion happens late in the movement, i.e. after the cursor has reached the expanded target area or even after it has reached the original target area. Participants’ subjective feedback on the target expansion are generally favorable, and this lends further support for the technique.

Interactive sensory objects for improving access to heritage

In this project we explore how to enhance the experience and understanding of cultural heritage in museums and heritage sites by creating interactive multisensory objects collaboratively with artists, technologists and people with learning disabilities. We focus here on workshops conducted during the first year of a three year project in which people with learning disabilities each constructed a 'sensory box' to represent their experiences of Speke Hall, a heritage site in the UK. The box is developed further in later workshops which explore aspects of physicality and how to appeal to the entire range of senses, making use of Arduino technology and basic sensors to enable an interactive user experience.

Categorization and construction of rule based systems

Expert systems have been increasingly popular for commercial importance. A rule based system is a special type of an expert system, which consists of a set of ‘if-then‘ rules and can be applied as a decision support system in many areas such as healthcare, transportation and security. Rule based systems can be constructed based on both expert knowledge and data. This paper aims to introduce the theory of rule based systems especially on categorization and construction of such systems from a conceptual point of view. This paper also introduces rule based systems for classification tasks in detail.

State of the art of learning styles-based adaptive educational hypermedia systems (Ls-Baehss)

The notion that learning can be enhanced when a teaching approach matches a learner’s learning style has been widely accepted in classroom settings since the latter represents a predictor of student’s attitude and preferences. As such, the traditional approach of ‘one-size-fits-all’ as may be applied to teaching delivery in Educational Hypermedia Systems (EHSs) has to be changed with an approach that responds to users’ needs by exploiting their individual differences. However, establishing and implementing reliable approaches for matching the teaching delivery and modalities to learning styles still represents an innovation challenge which has to be tackled. In this paper, seventy six studies are objectively analysed for several goals. In order to reveal the value of integrating learning styles in EHSs, different perspectives in this context are discussed. Identifying the most effective learning style models as incorporated within AEHSs. Investigating the effectiveness of different approaches for modelling students’ individual learning traits is another goal of this study. Thus, the paper highlights a number of theoretical and technical issues of LS-BAEHSs to serve as a comprehensive guidance for researchers who interest in this area.

Energy-efficient resource allocation in OFDM systems with distributed antennas

In this paper, we develop an energy-efficient resource-allocation scheme with proportional fairness for downlink multiuser orthogonal frequency-division multiplexing (OFDM) systems with distributed antennas. Our aim is to maximize energy efficiency (EE) under the constraints of the overall transmit power of each remote access unit (RAU), proportional fairness data rates, and bit error rates (BERs). Because of the nonconvex nature of the optimization problem, obtaining the optimal solution is extremely computationally complex. Therefore, we develop a low-complexity suboptimal algorithm, which separates subcarrier allocation and power allocation. For the low-complexity algorithm, we first allocate subcarriers by assuming equal power distribution. Then, by exploiting the properties of fractional programming, we transform the nonconvex optimization problem in fractional form into an equivalent optimization problem in subtractive form, which includes a tractable solution. Next, an optimal energy-efficient power-allocation algorithm is developed to maximize EE while maintaining proportional fairness. Through computer simulation, we demonstrate the effectiveness of the proposed low-complexity algorithm and illustrate the fundamental trade off between energy and spectral-efficient transmission designs.