Response of a weed community to different management practice in field bean

Weed control strategies for field beans were studied in North-eastern Croatia. This study focused on how different weed management practices affect weed community composition. The recommended pre-emergence herbicide application was compared to different treatments of post-emergence herbicide (broadcasted or banded over crop rows) and mechanical weed control in order to explore the response of a weed community to different management practice. Weed density data were used to compare total community densities by weed management strategies and to calculate diversity indices (Shannon's H', Shannon's E and Margalef's D-MG). Data were analyzed using ANOVA and multivariate technique. Weed community structure was generally similar in the post-emergence herbicide treatments, which were dominated by a few species that had high relative abundance values, while most of the species were of lower abundance. Notable fluctuations in weed communities corresponded with variation in weather patterns and management practice.

Can the study of natural vegetation succession assist in the control of soil erosion on abandoned croplands on the Loess Plateau, China?

In the Loess Plateau, China, arable cultivation of slope lands is common and associated with serious soil erosion. Planting trees or grass may control erosion, but planted species may consume more soil water and can threaten long-term ecosystem sustainability. Natural vegetation succession is an alternative ecological solution to restore degraded land, but there is a time cost, given that the establishment of natural vegetation, adequate to prevent soil erosion, is a longer process than planting. The aims of this study were to identify the environmental factors controlling the type of vegetation established on abandoned cropland and to identify candidate species that might be sown soon after abandonment to accelerate vegetation succession and establishment of natural vegetation to prevent soil erosion. A field survey of thirty-three 2 × 2–m plots was carried out in July 2003, recording age since abandonment, vegetation cover, and frequency of species together with major environmental and soil variables. Data were analyzed using correspondence analysis, classification tree analysis, and species response curves. Four vegetation types were identified and the data analysis confirmed the importance of time since abandonment, total P, and soil water in controlling the type of vegetation established. Among the dominant species in the three late-successional vegetation types, the most appropriate candidates for accelerating and directing vegetation succession were King Ranch bluestem (Bothriochloa ischaemum) and Lespedeza davurica (Leguminosae). These species possess combinations of the following characteristics: tolerance of low water and nutrient availability, fibrous root system and strong lateral vegetative spread, and a persistent seed bank.

Control of Leveillula taurica in tomato by Acremonium alternatum is by induction of resistance, not Hyperparasitism

Spores of the hyperparasite Acremonium alternatum reduced powdery mildew infection by Leveillula taurica on greenhouse tomato. The effect was slightly increased when spores were applied killed, and therefore not due to direct parasitism. The effect was systemic, protecting untreated leaves above the treated ones. Spores killed by heat had more effect than when killed by UV, so the effect was presumably due to induction of host resistance by substances released when cells were heat killed. The size of the effect depended upon leaf age and level of infection. Effects on primary infection and expansion of successful infections appear to be under independent control.

Fusing data-mining and knowledge elicitation for the supervisory control of an aluminium plate mill

Knowledge-elicitation is a common technique used to produce rules about the operation of a plant from the knowledge that is available from human expertise. Similarly, data-mining is becoming a popular technique to extract rules from the data available from the operation of a plant. In the work reported here knowledge was required to enable the supervisory control of an aluminium hot strip mill by the determination of mill set-points. A method was developed to fuse knowledge-elicitation and data-mining to incorporate the best aspects of each technique, whilst avoiding known problems. Utilisation of the knowledge was through an expert system, which determined schedules of set-points and provided information to human operators. The results show that the method proposed in this paper was effective in producing rules for the on-line control of a complex industrial process.

Design, implementation and testing of an intelligent knowledge-based system for the supervisory control of a hot rolling mill

This paper describes the design, implementation and testing of an intelligent knowledge-based supervisory control (IKBSC) system for a hot rolling mill process. A novel architecture is used to integrate an expert system with an existing supervisory control system and a new optimization methodology for scheduling the soaking pits in which the material is heated prior to rolling. The resulting IKBSC system was applied to an aluminium hot rolling mill process to improve the shape quality of low-gauge plate and to optimise the use of the soaking pits to reduce energy consumption. The results from the trials demonstrate the advantages to be gained from the IKBSC system that integrates knowledge contained within data, plant and human resources with existing model-based systems. (c) 2005 Elsevier Ltd. All rights reserved.

Speed sensorless vector control of induction machines

A novel rotor velocity estimation scheme applicable to vector controlled induction motors has been described. The proposed method will evaluate rotor velocity, ωr, on-line, does not require any extra transducers or injection of any signals, nor does it employ complicated algorithms such as MRAS or Kalman filters. Furthermore, the new scheme will operate at all velocities including zero with very little error. The procedure employs motor model equations, however all differential and integral terms have been eliminated giving a very fast, low-cost, effective and practical alternative to the current available methods. Simulation results verify the operation of the scheme under ideal and PWM conditions.

A stable one-step-ahead predictive control of non-linear systems

In this paper stability of one-step ahead predictive controllers based on non-linear models is established. It is shown that, under conditions which can be fulfilled by most industrial plants, the closed-loop system is robustly stable in the presence of plant uncertainties and input–output constraints. There is no requirement that the plant should be open-loop stable and the analysis is valid for general forms of non-linear system representation including the case out when the problem is constraint-free. The effectiveness of controllers designed according to the algorithm analyzed in this paper is demonstrated on a recognized benchmark problem and on a simulation of a continuous-stirred tank reactor (CSTR). In both examples a radial basis function neural network is employed as the non-linear system model.

Inverting recurrent neural networks for internal model control of nonlinear systems

In this paper, we show how a set of recently derived theoretical results for recurrent neural networks can be applied to the production of an internal model control system for a nonlinear plant. The results include determination of the relative order of a recurrent neural network and invertibility of such a network. A closed loop controller is produced without the need to retrain the neural network plant model. Stability of the closed-loop controller is also demonstrated.

Internal model control of nonlinear systems through the inversion of recurrent neural networks

Recurrent neural networks can be used for both the identification and control of nonlinear systems. This paper takes a previously derived set of theoretical results about recurrent neural networks and applies them to the task of providing internal model control for a nonlinear plant. Using the theoretical results, we show how an inverse controller can be produced from a neural network model of the plant, without the need to train an additional network to perform the inverse control.

Decentralized PID self-tuning control of industrial robots

A three degrees of freedom industrial robot is controlled by applying PID self-tuning (PID/ST) controllers. This control is considered as a corrective term to a nominal value, centrally computed from an inaccurate and/ or simplified dynamic model. An identification scheme on an assumed linear plant describing the deviation from the desired trajectory is employed in order to tune the controller coefficients and thus accomplish a behaviour prescribed through a desired pole placement. A salient feature of our approach is the decentralized nature of the controllers producing the corrective term for each joint. This opens the way to practical implementation, as recent computing requirement calculations for similar set-ups have shown in the literature. Numerical results are presented.

Automatic control of food manufacturing processes. 2nd edition

In the ten years since the first edition of this book appeared there have been significant developments in food process engineering, notably in biotechnology and membrane application. Advances have been made in the use of sensors for process control, and the growth of information technology and on-line computer applications continues apace. In addition, plant investment decisions are increasingly determined by quality assurance considerations and have to incorporate a greater emphasis on health and safety issues. The content of this edition has been rearranged to include descriptions of recent developments and to reflect the influence of new technology on the control and operations of automated plant. Original examples have been retained where relevant and these, together with many new illustrations, provide a comprehensive guide to good practice.

Endocannabinoids in neuroendopsychology: multiphasic control of mitochondrial function

The endocannabinoid system (ECS) is a construct based on the discovery of receptors that are modulated by the plant compound tetrahydrocannabinol and the subsequent identification of a family of nascent ligands, the 'endocannabinoids'. The function of the ECS is thus defined by modulation of these receptors-in particular, by two of the best-described ligands (2-arachidonyl glycerol and anandamide), and by their metabolic pathways. Endocannabinoids are released by cell stress, and promote both cell survival and death according to concentration. The ECS appears to shift the immune system towards a type 2 response, while maintaining a positive energy balance and reducing anxiety. It may therefore be important in resolution of injury and inflammation. Data suggest that the ECS could potentially modulate mitochondrial function by several different pathways; this may help explain its actions in the central nervous system. Dose-related control of mitochondrial function could therefore provide an insight into its role in health and disease, and why it might have its own pathology, and possibly, new therapeutic directions.

A decade of plant proteomics and mass spectrometry: translation of technical advancements to food security and safety issues

Tremendous progress in plant proteomics driven by mass spectrometry (MS) techniques has been made since 2000 when few proteomics reports were published and plant proteomics was in its infancy. These achievements include the refinement of existing techniques and the search for new techniques to address food security, safety, and health issues. It is projected that in 2050, the world’s population will reach 9–12 billion people demanding a food production increase of 34–70% (FAO, 2009) from today’s food production. Provision of food in a sustainable and environmentally committed manner for such a demand without threatening natural resources, requires that agricultural production increases significantly and that postharvest handling and food manufacturing systems become more efficient requiring lower energy expenditure, a decrease in postharvest losses, less waste generation and food with longer shelf life. There is also a need to look for alternative protein sources to animal based (i.e., plant based) to be able to fulfill the increase in protein demands by 2050. Thus, plant biology has a critical role to play as a science capable of addressing such challenges. In this review, we discuss proteomics especially MS, as a platform, being utilized in plant biology research for the past 10 years having the potential to expedite the process of understanding plant biology for human benefits. The increasing application of proteomics technologies in food security, analysis, and safety is emphasized in this review. But, we are aware that no unique approach/technology is capable to address the global food issues. Proteomics-generated information/resources must be integrated and correlated with other omics-based approaches, information, and conventional programs to ensure sufficient food and resources for human development now and in the future.

Control of final moisture content of food products baked in continuous tunnel ovens

There are well-known difficulties in making measurements of the moisture content of baked goods (such as bread, buns, biscuits, crackers and cake) during baking or at the oven exit; in this paper several sensing methods are discussed, but none of them are able to provide direct measurement with sufficient precision. An alternative is to use indirect inferential methods. Some of these methods involve dynamic modelling, with incorporation of thermal properties and using techniques familiar in computational fluid dynamics (CFD); a method of this class that has been used for the modelling of heat and mass transfer in one direction during baking is summarized, which may be extended to model transport of moisture within the product and also within the surrounding atmosphere. The concept of injecting heat during the baking process proportional to the calculated heat load on the oven has been implemented in a control scheme based on heat balance zone by zone through a continuous baking oven, taking advantage of the high latent heat of evaporation of water. Tests on biscuit production ovens are reported, with results that support a claim that the scheme gives more reproducible water distribution in the final product than conventional closed loop control of zone ambient temperatures, thus enabling water content to be held more closely within tolerance.