Silicon, the silver bullet for mitigating biotic and abiotic stress, and improving grain quality, in rice?

Adequate silicon fertilization greatly boosts rice yield and mitigates biotic and abiotic stress, and improves grain quality through lowering the content of cadmium and inorganic arsenic. This review on silicon dynamics in rice considers recent advances in our understanding of the role of silicon in rice, and the challenges of maintaining adequate silicon fertility within rice paddy systems. Silicon is increasingly considered as an element required for optimal plant performance, particularly in rice. Plants can survive with very low silicon under laboratory/glasshouse conditions, but this is highly artificial and, thus, silicon can be considered as essential for proper plant function in its environment. Silicon is incorporated into structural components of rice cell walls were it increases cell and tissue rigidity in the plant. Structural silicon provides physical protection to plants against microbial infection and insect attack as well as reducing the quality of the tissue to the predating organisms. The abiotic benefits are due to silicon's effect on overall organ strength. This helps protect against lodging, drought stress, high temperature (through efficient maintenance of transpiration), and photosynthesis by protecting against high UV. Furthermore, silicon also protects the plant from saline stress and against a range of toxic metal stresses (arsenic, cadmium, chromium, copper, nickel and zinc). Added to this, silicon application decreases grain concentrations of various human carcinogens, in particular arsenic, antimony and cadmium. As rice is efficient at stripping bioavailable silicon from the soil, recycling of silicon rich rice straw biomass or addition of inorganic silicon fertilizer, primarily obtained from iron and steel slag, needs careful management. Silicon in the soil may be lost if the silicon-cycle, traditionally achieved via composting of rice straw and returning it to the land, is being broken. As composting of rice straw and incorporation of composted or non-composted straw back to land are resource intensive activities, these activities are declining due to population shifts from the countryside to cities. Processes that accelerate rice straw composting, therefore, need to be identified to aid more efficient use of this resource. In addition, rice genetics may help address declining available silicon in paddy soils: for example by selecting for characteristics during breeding that lead to an increased ability of roots to access recalcitrant silicon sources from soil and/or via selection for traits that aid the maintenance of a high silicon status in shoots. Recent advances in understanding the genetic regulation of silicon uptake and transport by rice plants will aid these goals.

Non-parametric bill of quantities estimation of concrete road bridges' superstructure: an artificial neural networks approach

Bridge construction responds to the need for environmentally friendly design of motorways and facilitates the passage through sensitive natural areas and the bypassing of urban areas. However, according to numerous research studies, bridge construction presents substantial budget overruns. Therefore, it is necessary early in the planning process for the decision makers to have reliable estimates of the final cost based on previously constructed projects. At the same time, the current European financial crisis reduces the available capital for investments and financial institutions are even less willing to finance transportation infrastructure. Consequently, it is even more necessary today to estimate the budget of high-cost construction projects -such as road bridges- with reasonable accuracy, in order for the state funds to be invested with lower risk and the projects to be designed with the highest possible efficiency. In this paper, a Bill-of-Quantities (BoQ) estimation tool for road bridges is developed in order to support the decisions made at the preliminary planning and design stages of highways. Specifically, a Feed-Forward Artificial Neural Network (ANN) with a hidden layer of 10 neurons is trained to predict the superstructure material quantities (concrete, pre-stressed steel and reinforcing steel) using the width of the deck, the adjusted length of span or cantilever and the type of the bridge as input variables. The training dataset includes actual data from 68 recently constructed concrete motorway bridges in Greece. According to the relevant metrics, the developed model captures very well the complex interrelations in the dataset and demonstrates strong generalisation capability. Furthermore, it outperforms the linear regression models developed for the same dataset. Therefore, the proposed cost estimation model stands as a useful and reliable tool for the construction industry as it enables planners to reach informed decisions for technical and economic planning of concrete bridge projects from their early implementation stages.

Factores que influenciam o peso e crescimento de cabritos da raça Murciana-granadina, em aleitamento artificial

A exploração caprina de leite tem evoluído no sentido de alguma intensificação, com recurso a raças de elevado potencial produtivo, de que é exemplo a raça Murciana- Granadina. O leite constitui a principal fonte de receita destas explorações. Complementarmente, vendem animais para carne e, as de melhor nível genético, animais para reprodutores. Analisaram-se os pesos de 241 cabritos da raça Murciana-Granadina, numa exploração comercial, com o objectivo de quantificar os pesos e crescimento de cabritos, e identificar os factores que os influenciam. Os cabritos foram aleitados artificialmente, em regime ad libitum, com leite de substituição comercial, dispondo ainda de concentrado comercial, feno de luzerna e palha. Os cabritos foram pesados ao nascimento e, posteriormente, semanalmente, até aos 60 dias de idade. Calcularam-se os respetivos pesos ajustados, bem como os ganhos médios diários, a diferentes idades padrão. Procedeu-se a uma análise de variância com um modelo linear que incluiu os efeitos da época de parto, tipo de parto, sexo e idade da cabra. Foram registados pesos superiores nos partos simples e duplos, relativamente aos triplos, e nos machos, relativamente às fêmeas. Os ganhos médios diários, a partir do mês de idade, registaram valores inferiores na época inverno-primavera, comparativamente com a época primavera-verão. Dairy goat farming has evolved towards intensification, with increased use of high milk-yielding breeds, including the Murciano-Granadina breed. Milk is the main source of farm income. Secondary income sources are the sale of animals for meat and, in genetically superior herds, the sale of breeding animals. The weights of 241 commercial farms artificially reared Murciano-Granadina kids were analyzed with the objective of quantifying weight and growth and identifying variation factors. Kids were artificially reared to weaning, on ad libitum commercial milk replacer, commercial concentrate, lucerne hay and straw. Kids were weighed at birth and at weekly intervals until 60 days of age. Age adjusted weights and growth-rates were calculated. A variance analysis was performed with a model including the effects of season of birth, number of kids per kidding, sex and age of dam. Single and twin-born kids had higher weights than triplets, and males had higher weights than females. Average daily gain after one month of age was lower for kids born in winter-spring than for those born in spring-summer

Logic Programming and Artificial Neural Networks in Pharmacological Screening of Schinus Essential Oils

Some plants of genus Schinus have been used in the folk medicine as topical antiseptic, digestive, purgative, diuretic, analgesic or antidepressant, and also for respiratory and urinary infections. Chemical composition of essential oils of S. molle and S. terebinthifolius had been evaluated and presented high variability according with the part of the plant studied and with the geographic and climatic regions. The pharmacological properties, namely antimicrobial, anti-tumoural and anti-inflammatory activities are conditioned by chemical composition of essential oils. Taking into account the difficulty to infer the pharmacological properties of Schinus essential oils without hard experimental approach, this work will focus on the development of a decision support system, in terms of its knowledge representation and reasoning procedures, under a formal framework based on Logic Programming, complemented with an approach to computing centered on Artificial Neural Networks and the respective Degree-of-Confidence that one has on such an occurrence.

Soft-computing techniques applied to artificial tissue temperature estimation

Tese dout., Engenharia electrónica e computação - Processamento de sinal, Universidade do Algarve, 2008

Experimental study of the macrobenthic colonisation and secondary production in the artificial reefs of Algarve coast

Tese de dout., Ciências do Mar, Faculdade de Ciências do Mar e do Ambiente, Universidade do Algarve, 2010

Effect of high CO2 and ocean acidification on photosynthesis and response to oxidative stress in seagrasses

Climate change scenarios comprise significant modifications of the marine realm, notably ocean acidification and temperature increase, both direct consequences of the rising atmospheric CO2 concentration. These changes are likely to impact marine organisms and ecosystems, namely the valuable seagrass-dominated coastal habitats. The main objective of this thesis was to evaluate the photosynthetic and antioxidant responses of seagrasses to climate change, considering CO2, temperature and light as key drivers of these processes. The methodologies used to determine global antioxidant capacity and antioxidant enzymatic activity in seagrasses were optimized for the species Cymodocea nodosa and Posidonia oceanica, revealing identical defence mechanisms to those found in terrestrial plants. The detailed analysis and identification of photosynthetic pigments in Halophila ovalis, H.stipulacea, Zostera noltii, Z marina, Z. capricorni, Cymodocea nodosa and Posidonia oceanica, sampled across different climatic zones and depths, also revealed a similarity with terrestrial plants, both in carotenoid composition and in the pigment-based photoprotection mechanisms. Cymodocea nodosa plants from Ria Formosa were submitted to the combined effect of potentially stressful light and temperature ranges and showed considerable physiological tolerance, due to the combination of changes in the antioxidant system, activation of the VAZ cycle and accumulation of leaf soluble sugars, thus preventing the onset of oxidative stress. Cymodocea nodosa plants living in a naturally acidified environment near submarine volcanic vents in Vulcano Island (Italy) showed to be under oxidative stress despite the enhancement of the antioxidant capacity, phenolics concentration and carotenoids. Posidonia oceanica leaves loaded with epiphytes showed a significant increase in oxidative stress, despite the increase of antioxidant responses and the allocation of energetic resources to these protection mechanisms. Globally, the results show that seagrasses are physiologically able to deal with potentially stressful conditions from different origins, being plastic enough to avoid stress in many situations and to actively promote ulterior defence and repair mechanisms when under effective oxidative stress.

Introdução à inteligência artificial com recurso à programação visual

Relatório da Prática de Ensino Supervisionada, Mestrado em Ensino da Matemática, Universidade de Lisboa, Instituto de Educação, 2014

Impacto da iluminação natural e artificial na disponibilidade de macro-invertebrados bentónicos para as aves limícolas

Tese de mestrado em Ecologia Marinha, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2015

An artificial neural network approach for short-term wind power forecasting in Portugal

This paper presents an artificial neural network approach for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. The accuracy of the wind power forecasting attained with the proposed approach is evaluated against persistence and ARIMA approaches, reporting the numerical results from a real-world case study.

Dynamic artificial neural network for electricity market prices forecast

This paper presents an artificial neural network applied to the forecasting of electricity market prices, with the special feature of being dynamic. The dynamism is verified at two different levels. The first level is characterized as a re-training of the network in every iteration, so that the artificial neural network can able to consider the most recent data at all times, and constantly adapt itself to the most recent happenings. The second level considers the adaptation of the neural network’s execution time depending on the circumstances of its use. The execution time adaptation is performed through the automatic adjustment of the amount of data considered for training the network. This is an advantageous and indispensable feature for this neural network’s integration in ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to the market negotiating players of MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).

Scheduling distributed energy resources in an isolated grid - an artificial neural network approach

Power Systems (PS), have been affected by substantial penetration of Distributed Generation (DG) and the operation in competitive environments. The future PS will have to deal with large-scale integration of DG and other distributed energy resources (DER), such as storage means, and provide to market agents the means to ensure a flexible and secure operation. Virtual power players (VPP) can aggregate a diversity of players, namely generators and consumers, and a diversity of energy resources, including electricity generation based on several technologies, storage and demand response. This paper proposes an artificial neural network (ANN) based methodology to support VPP resource schedule. The trained network is able to achieve good schedule results requiring modest computational means. A real data test case is presented.