Heavy metal toxicity in rice and soybean plants cultivated in contaminated soil

Heavy metals can accumulate in soil and cause phytotoxicity in plants with some specific symptoms. The present study evaluated the specific symptoms on rice and soybeans plants caused by excess of heavy metals in soil. Rice and soybean were grown in pots containing soil with different levels of heavy metals. A completely randomized design was used, with four replications, using two crop species and seven sample soils with different contamination levels. Rice and soybean exhibited different responses to the high concentrations of heavy metals in the soil. Rice plants accumulated higher Cu, Mn, Pb and Zn concentrations and were more sensitive to high concentrations of these elements in the soil, absorbing them more easily compared to the soybean plants. However, high available Zn concentrations in the soil caused phytotoxicity symptoms in rice and soybean, mainly chlorosis and inhibited plant growth. Further, high Zn concentrations in the soil reduced the Fe concentration in the shoots of soybean and rice plants to levels considered deficient.

Sensor element for a metal-insulator-semiconductor camera system (MISCam)

We discuss the operation of a new type of optical sensor (MISCam) based on a metal-insulator-semiconductor (MIS) structure. The operation principle relies on light-induced changes of the band bending and barrier height at the interface between semiconductor and insulator. An image is obtained from the quenching of the ac signal in analogy to the principle of the laser-scanned photodiode (LSP). Lateral resolution depends on the semiconductor material chosen. We have characterised the MIS structures by C-V, I-V, and spectral response measurements testing different types of insulators like a-Si3N4, SiO2, and AlN. The presence of slow interface charges allows for image memory. Colour sensors can be realised by controlling sign and magnitude of the electric fields in the base and the interface region.

Coexistence of Universal and Topological Anomalous Hall Effects in Metal CrO2 Thin Films in the Dirty Limit

The scaling exponent of 1.6 between anomalous Hall and longitudinal conductivity, characteristic of the universal Hall mechanism in dirty-metal ferromagnets, emerges from a series of CrO2 films as we systematically increase structural disorder. Magnetic disorder in CrO2 increases with temperature and this drives a separate topological Hall mechanism. We find that these terms are controlled discretely by structural and magnetic defect populations, and their coexistence leads to apparent divergence from exponent 1.6, suggesting that the universal term is more prevalent than previously realized.

Chromatographic behaviour of monoclonal antibodies against wild-type amidase from Pseudomonasaeruginosa on immobilized metal chelates

The aim of this work was to devise a one-step purification procedure for monoclonal antibodies (MAbs) of IgG class by immobilized metal affinity chromatography (IMAC). Therefore, several stationary phases were prepared containing immobilized metal chelates in order to study the chromatographic behaviour of MAbs against wild-type amidase from Pseudomonas aeruginosa. Such MAbs adsorbed to Cu(II), Ni(II), Zn(II) and Co(II)-IDA agarose columns. The increase in ligand concentration and the use of longer spacer arms and higher pH values resulted in higher adsorption of MAbs into immobilized metal chelates. The dynamic binding capacity and the maximum binding capacity were 1.33 +/- 0.015 and 3.214 +/- 0.021 mg IgG/mL of sedimented commercial matrix, respectively. A K(D) of 4.53 x 10(-7) M was obtained from batch isotherm measurements. The combination of tailor-made stationary phases of IMAC and the correct selection of adsorption conditions permitted a one-step purification procedure to be devised for MAbs of IgG class. Culture supernatants containing MAbs were purified by IMAC on commercial-Zn(II) and EPI-30-IDA-Zn(II) Sepharose 6B columns and by affinity chromatography on Protein A-Sepharose CL-4B. This MAb preparation revealed on SDS-PAGE two protein bands with M(r) of 50 and 22 kDa corresponding to the heavy and light chains, respectively. Copyright (C) 2011 John Wiley & Sons, Ltd.

Intelligent Bio-Inspired system for manufacturing scheduling under uncertainties

A novel approach to scheduling resolution by combining Autonomic Computing (AC), Multi-Agent Systems (MAS), Case-based Reasoning (CBR), and Bio-Inspired Optimization Techniques (BIT) will be described. AC has emerged as a paradigm aiming at incorporating applications with a management structure similar to the central nervous system. The main intentions are to improve resource utilization and service quality. In this paper we envisage the use of MAS paradigm for supporting dynamic and distributed scheduling in Manufacturing Systems with AC properties, in order to reduce the complexity of managing manufacturing systems and human interference. The proposed CBR based Intelligent Scheduling System was evaluated under different dynamic manufacturing scenarios.

Self-optimization for dynamic scheduling in manufacturing systems

Scheduling is a critical function that is present throughout many industries and applications. A great need exists for developing scheduling approaches that can be applied to a number of different scheduling problems with significant impact on performance of business organizations. A challenge is emerging in the design of scheduling support systems for manufacturing environments where dynamic adaptation and optimization become increasingly important. In this paper, we describe a Self-Optimizing Mechanism for Scheduling System through Nature Inspired Optimization Techniques (NIT).

MASDSheGATS – Scheduling System for Dynamic Manufacturing Environments

This chapter addresses the resolution of scheduling in manufacturing systems subject to perturbations. The planning of Manufacturing Systems involves frequently the resolution of a huge amount and variety of combinatorial optimisation problems with an important impact on the performance of manufacturing organisations. Examples of those problems are the sequencing and scheduling problems in manufacturing management, routing and transportation, layout design and timetabling problems.

Self-organization for scheduling in agile manufacturing

Agility refers to the manufacturing system ability to rapidly adapt to market and environmental changes in efficient and cost-effective ways. This paper addresses the development of self-organization methods to enhance the operations of a scheduling system, by integrating scheduling system, configuration and optimization into a single autonomic process requiring minimal manual intervention to increase productivity and effectiveness while minimizing complexity for users. We intend to conceptualize real manufacturing systems as interacting autonomous entities in order to build future Decision Support Systems (DSS) for Scheduling in agile manufacturing environments.

Collective intelligence on dynamic manufacturing scheduling optimization

Swarm Intelligence (SI) is a growing research field of Artificial Intelligence (AI). SI is the general term for several computational techniques which use ideas and get inspiration from the social behaviours of insects and of other animals. This paper presents hybridization and combination of different AI approaches, like Bio-Inspired Techniques (BIT), Multi-Agent systems (MAS) and Machine Learning Techniques (ML T). The resulting system is applied to the problem of jobs scheduling to machines on dynamic manufacturing environments.

Autonomic computing for scheduling in manufacturing systems

We describe a novel approach to scheduling resolution by combining Autonomic Computing (AC), Multi-Agent Systems (MAS) and Nature Inspired Optimization Techniques (NIT). Autonomic Computing has emerged as paradigm aiming at embedding applications with a management structure similar to a central nervous system. A natural Autonomic Computing evolution in relation to Current Computing is to provide systems with Self-Managing ability with a minimum human interference. In this paper we envisage the use of Multi-Agent Systems paradigm for supporting dynamic and distributed scheduling in Manufacturing Systems with Autonomic properties, in order to reduce the complexity of managing systems and human interference. Additionally, we consider the resolution of realistic problems. The scheduling of a Cutting and Treatment Stainless Steel Sheet Line will be evaluated. Results show that proposed approach has advantages when compared with other scheduling systems.

Cooperative intelligent system for manufacturing scheduling

Hybridization of intelligent systems is a promising research field of computational intelligence focusing on combinations of multiple approaches to develop the next generation of intelligent systems. In this paper we will model a Manufacturing System by means of Multi-Agent Systems and Meta-Heuristics technologies, where each agent may represent a processing entity (machine). The objective of the system is to deal with the complex problem of Dynamic Scheduling in Manufacturing Systems.