Modeling trade-offs among ecosystem services in agricultural production systems

Although agricultural ecosystems can provide humans with a wide set of benefits agricultural production system management is mainly driven by food production. As a consequence, a need to ensure food security globally has been accompanied by a significant decline in the state of ecosystems. In order to reduce negative trade-offs and identify potential synergies it is necessary to improve our understanding of the relationships between various ecosystem services (ES) as well as the impacts of farm management on ES provision. We present a spatially explicit application that captures and quantifies ES trade-offs in the crop systems of Llanada Alavesa in the Basque Country. Our analysis presents a quantitative assessment of selected ES including crop yield, water supply and quality, climate regulation and air quality. The study is conducted using semantic meta-modeling, a technique that enables flexible integration of models to overcome the service-by-service modeling approach applied traditionally in ES assessment.     

Multilevel adaptive control of nonlinear interconnected systems

This paper presents an adaptive backstepping-based multilevel approach for the first time to control nonlinear interconnected systems with unknown parameters. The system consists of a nonlinear controller at the first level to neutralize the interaction terms, and some adaptive controllers at the second level, in which the gains are optimally tuned using genetic algorithm. The presented scheme can be used in systems with strong couplings where completely ignoring the interactions leads to problems in performance or stability. In order to test the suitability of the method, two case studies are provided: the uncertain double and triple coupled inverted pendulums connected by springs with unknown parameters. The simulation results show that the method is capable of controlling the system effectively, in both regulation and tracking tasks.     

State feedback integral control for a rotary direct drive servo valve using a Lyapunov function approach

This paper concerns a state feedback integral control using a Lyapunov function approach for a rotary direct drive servo valve (RDDV) while considering parameter uncertainties. Modeling of this RDDV servovalve reveals that its mechanical performance is deeply influenced by friction torques and flow torques; however, these torques are uncertain and mutable due to the nature of fluid flow. To eliminate load resistance and to achieve satisfactory position responses, this paper develops a state feedback control that integrates an integral action and a Lyapunov function. The integral action is introduced to address the nonzero steady-state error; in particular, the Lyapunov function is employed to improve control robustness by adjusting the varying parameters within their value ranges. This new controller also has the advantages of simple structure and ease of implementation. Simulation and experimental results demonstrate that the proposed controller can achieve higher control accuracy and stronger robustness.     

Control of self-assembly in micro- and nano-scale systems

Control of self-assembling systems at the micro- and nano-scale provides new opportunities for the engineering of novel materials in a bottom-up fashion. These systems have several challenges associated with control including high-dimensional and stochastic nonlinear dynamics, limited sensors for real-time measurements, limited actuation for control, and kinetic trapping of the system in undesirable configurations. Three main strategies for addressing these challenges are described, which include particle design (active self-assembly), open-loop control, and closed-loop (feedback) control. The strategies are illustrated using a variety of examples such as the design of patchy and Janus particles, the toggling of magnetic fields to induce the crystallization of paramagnetic colloids, and high-throughput crystallization of organic compounds in nanoliter droplets. An outlook of the future research directions and the necessary technological advancements for control of micro- and nano-scale self-assembly is provided.     

Programming assistance tools to support the learning of IT programming in South African secondary schools

Novice programmers struggle to understand introductory programming concepts and this difficulty, associated with learning to program, contributes mainly to the lack of interest in the field of Computer Science at tertiary level. Programming assistance tools have been used to assist novice programmers extensively at education institutions. A programming assistance tool (PAT) is a software program that can be used by novice programmers to learn how to program and/or improve their understanding of programming concepts.This research proposes that novice programmers, specifically Information Technology (IT) scholars in South African secondary schools, could be supported by PATs. The main objective of this research was to determine whether the use of a PAT impacted IT scholars' understanding of programming concepts and their motivation towards programming in general. Criteria for the selection of PATs were derived from the programming difficulties identified in literature and from surveys to IT teachers and scholars. The selection criteria were grouped into programming knowledge and programming skills categories. Existing PATs were evaluated using the selection criteria and three PATs, namely, RoboMind, Scratch and B#, were selected for evaluation in this research study. A convenience sample of schools participated in the study. The three PATs provided different approaches while being able to support the Delphi programming language used in schools that participated in the study.The findings of this research indicated that, although scholars perceived the PATs to be useful in the explanation of certain of the programming concepts, there was no conclusive evidence that IT scholars who used a PAT had a significantly better understanding of programming concepts and motivation towards programming than scholars who did not use a PAT. Participant feedback was used to identify the strengths and shortcomings of the three PATs and to provide recommendations for the development of future PATs specifically designed to support IT scholars.     

Adolescents’ comments in social media: Why do adolescents receive negative feedback and who is most at risk?

Receiving negative peer feedback in social media may have negative consequences for adolescents’ psychosocial development and well-being. Therefore, the first aim of this study was to investigate online behavior (i.e., online social exploration, risky online self-presentation) that predicts receiving negative online peer feedback. The second aim was to examine three types of precursors that may predict this online behavior and, indirectly, negative feedback: (a) developmental (i.e., sex, age), (b) dispositional (i.e., sensation seeking, inhibitory control), and (c) social precursors (i.e., peer problems, family conflict). We collected survey data among 785 Dutch adolescents (10–15 years old). Our results showed that adolescents who engaged in online social exploration and risky online self-presentation more often, were more likely to receive negative peer feedback. Online social exploration was more prevalent among the older adolescents and adolescents characterized by higher sensation seeking and more family conflict. In addition, risky online self-presentation was more prevalent among adolescents high in sensation seeking. Consequently, these adolescents’ online behavior, indirectly, made them more at risk of receiving negative peer feedback in social media.     

Small-gain stability conditions for linear systems with time-varying delays

In this paper we show that a variety of stability conditions, both existing and new, can be derived for linear systems subject to time-varying delays in a unified manner in the form of scaled small-gain conditions. From a robust control perspective, our development seeks to cast the stability problem as one of robust stability analysis, and the resulting stability conditions are also reminiscent of robust stability bounds typically found in robust control theory. The development is built on the well-known conventional robust stability analysis, requiring essentially no more than a straightforward application of the small gain theorem. The derived conditions have conceptual appeal, and they can be checked using standard robust control toolboxes.     

Analysis and decomposition of active and reactive power spot price in deregulated electricity markets

Electricity markets have suffered important modifications in recent decades in many countries, in which a competition framework has been established with the aim of improving market efficiency and reducing energy prices. However, this new paradigm does not assure optimal solutions, as new constraints can be introduced in optimization processes that can affect the resulting prices. An example of this situation is the establishment of power purchase agreements between producers and consumers. A wide literature can be found regarding electricity markets. Some of this literature refers to the theory of spot prices and its application to them. This paper deals with the obtaining, decomposition and deduction of behavior rules of spot prices, and their influence on established contractual relationships in a deregulated market environment which allows power purchase agreements between consumers and producers. It is performed by a deterministic modeling of the complete generation-grid system. The influence of the existence of this kind of agreements on both total costs and spot prices is discussed.     

Nonlinear adaptive filtering using kernel‐based algorithms with dictionary adaptation

Nonlinear adaptive filtering has been extensively studied in the literature, using, for example, Volterra filters or neural networks. Recently, kernel methods have been offering an interesting alternative because they provide a simple extension of linear algorithms to the nonlinear case. The main drawback of online system identification with kernel methods is that the filter complexity increases with time, a limitation resulting from the representer theorem, which states that all past input vectors are required. To overcome this drawback, a particular subset of these input vectors (called dictionary) must be selected to ensure complexity control and good performance. Up to now, all authors considered that, after being introduced into the dictionary, elements stay unchanged even if, because of nonstationarity, they become useless to predict the system output. The objective of this paper is to present an adaptation scheme of dictionary elements, which are considered here as adjustable model parameters, by deriving a gradient‐based method under collinearity constraints. The main interest is to ensure a better tracking performance. To evaluate our approach, dictionary adaptation is introduced into three well‐known kernel‐based adaptive algorithms: kernel recursive least squares, kernel normalized least mean squares, and kernel affine projection. The performance is evaluated on nonlinear adaptive filtering of simulated and real data sets. As confirmed by experiments, our dictionary adaptation scheme allows either complexity reduction or a decrease of the instantaneous quadratic error, or both simultaneously. Copyright © 2015 John Wiley & Sons, Ltd.