A Survey of the Individual-Based Model applied in Biomedical and Epidemiology

Individual-based model (IBM) has been used to simulate and to design control strategies for dynamic systems that are subject to stochasticity and heterogeneity, such as infectious diseases. In the IBM, an individual is represented by a set of specific characteristics that may change dynamically over time. This feature allows a more realistic analysis of the spread of an epidemic. This paper presents a literature survey of IBM applied to biomedical and epidemiology research. The main goal is to present existing techniques, advantages and future perspectives in the development of the model. We evaluated 89 articles, which mostly analyze interventions aimed at endemic infections. In addition to the review, an overview of IBM is presented as an alternative to complement or replace compartmental models, such as the SIR (Susceptible-Infected-Recovered) model. Numerical simulations also illustrate the capabilities of IBM, as well as some limitations regarding the effects of discretization. We show that similar side-effects of discretization scheme for compartmental models may also occur in IBM, which requires careful attention

Bagging to protect calla lily flowers against stingless bee (Trigona spinipes)

Stingless bee Trigona spinipes (Hymenoptera: Apidae) is an important pest of calla lily, Zantedeschia aethiopica (L.), damaging flowers, especially the spadix. The aim was to identify the most efficient packaging for bagging calla lily inflorescences, aiming to protect against the attack of stingless bee and to maintain postharvest quality. The experiment was carried out in a calla lily plantation cultivated in soil under 50% shading screen. Treatments consisted in bagging calla lily flowers with: 1) brown kraft paper bag, 2) non-woven fabric (NWF) bag; 3) transparent plastic bag, 4) transparent micro-perforated plastic bag and 5) control (without bagging). The experimental design was completely randomized with 25 replicates and one inflorescence per plot. Inflorescences received treatments when they presented definitive color, but still with completely closed spathe. Seven days after bagging, inflorescences were collected and evaluated for damages caused by insects in the field and the postharvest characteristics. Postharvest quality evaluations of inflorescences were performed for 12 days, observing expansion of the spathe in length and width, stem weight and visual quality expressed by the number of days that remained in each class. The bagging of calla lily inflorescences was efficient in the control of stingless bee, regardless of packaging used, because under these conditions, no inflorescence presented damage. In control, 84% of damaged inflorescences were observed. Differences in postharvest characteristics were observed and inflorescences remained for longer periods in the process of spathe opening, which is characterized by the measurement of their length and width, when packed. Among packages, NWF allowed longer spathe length at the 6th day of evaluation, larger width at 7th day of evaluation and less fresh mass loss at the end of the experiment (8%). In control, reduction of spathe measurements from the first day of evaluation and loss of 11% of fresh mass were observed. It was concluded that NWF is an efficient packaging to protect calla lily against the attack of stingless bee without compromising the postharvest quality of inflorescences

A Survey of the Individual-Based Model applied in Biomedical and Epidemiology

Individual-based model (IBM) has been used to simulate and to design control strategies for dynamic systems that are subject to stochasticity and heterogeneity, such as infectious diseases. In the IBM, an individual is represented by a set of specific characteristics that may change dynamically over time. This feature allows a more realistic analysis of the spread of an epidemic. This paper presents a literature survey of IBM applied to biomedical and epidemiology research. The main goal is to present existing techniques, advantages and future perspectives in the development of the model. We evaluated 89 articles, which mostly analyze interventions aimed at endemic infections. In addition to the review, an overview of IBM is presented as an alternative to complement or replace compartmental models, such as the SIR (Susceptible-Infected-Recovered) model. Numerical simulations also illustrate the capabilities of IBM, as well as some limitations regarding the effects of discretization. We show that similar side-effects of discretization scheme for compartmental models may also occur in IBM, which requires careful attention

A Survey of the Individual-Based Model applied in Biomedical and Epidemiology

Individual-based model (IBM) has been used to simulate and to design control strategies for dynamic systems that are subject to stochasticity and heterogeneity, such as infectious diseases. In the IBM, an individual is represented by a set of specific characteristics that may change dynamically over time. This feature allows a more realistic analysis of the spread of an epidemic. This paper presents a literature survey of IBM applied to biomedical and epidemiology research. The main goal is to present existing techniques, advantages and future perspectives in the development of the model. We evaluated 89 articles, which mostly analyze interventions aimed at endemic infections. In addition to the review, an overview of IBM is presented as an alternative to complement or replace compartmental models, such as the SIR (Susceptible-Infected-Recovered) model. Numerical simulations also illustrate the capabilities of IBM, as well as some limitations regarding the effects of discretization. We show that similar side-effects of discretization scheme for compartmental models may also occur in IBM, which requires careful attention

Filamentous fungi and agro-industrial residues selection for enzyme production of biotechnological interest<br>Seleção de fungos filamentosos e de resíduos agroindustriais para a produção de enzimas de interesse biotecnológico

Many fungal enzymes have relevant applications in different industrial areas. The objective of this work was to select fungi producing hydrolytic enzymes, as well as establish agro-industrial wastes capable of inducing higher production levels. Xylanase, endoglucanase, amylase and poligalacturonase activities were determined by incubating the culture filtrates with their respective substrates. Subsequently, the reducing sugars determination was carried out using 3,5-dinitrosalicylic acid reagent.The protein determination was performed according the modified Bradford method. Among the fungal strains evaluated, Aspergillus niger J4 showed higher levels of xylanase production (8.73 ± 0.34 U/ml) and this was greatest when brewer’s spent grain was used as substrate (9.80 ± 0.02 U/ml). Penicillium miczynskii produced the highest levels of endoglucanasic activity (0.13 ± 0.03 U/ml), which, in turn, was favored in the pineapple peel presence (0.18 ± 0.02 U/ml). In relation to amylase, A. niger J26 was selected as the best producer strain (6.10 ± 0.30 U/ml) with wheat bran as the best substrate for their production (7.32 ± 0.14 U/ml). Penicillium verruculosum exhibited the highest level of poligalacturonase activity (8.65 ± 0.12 U/ml)), especially when grown in orange peel presence (10.32 ± 0.10 U/ml). These wastes use in these enzymes production may not only reduce their production cost, but also substantially reduce the environmental impact caused by the deposition of these wastes on the environment.<p><p>Muitas enzimas produzidas por fungos têm relevantes aplicações em diferentes áreas industriais. O objetivo deste trabalho foi selecionar fungos filamentosos produtores de enzimas hidrolíticas, bem como estabelecer os resíduos agroindustriais capazes de induzir maiores níveis de produção. As atividades xilanásica, endoglucanásica, amilásica e poligalacturonásica foram determinadas incubandose os filtrados de cultura com seus respectivos substratos. Posteriormente, a determinação de açúcares redutores foi realizada utilizando-se o reagente ácido 3,5-dinitrosalicílico. A determinação de proteínas foi realizada segundo o método de Bradford modificado. Dentre as linhagens fúngicas avaliadas, Aspergillus niger J4 apresentou maiores níveis de produção de xilanases (8,73 ± 0,34 U/mL) e esta foi maior quando o bagaço de malte foi utilizado como substrato (9,80 ± 0,02 U/mL). Penicillium miczynskii produziu os índices mais elevados de atividade endoglucanásica (0,13 ± 0,03 U/mL), sendo está última favorecida na presença de casca de abacaxi (0,18 ± 0,02 U/mL). Em relação à amilase, A. niger J26 foi selecionada como a melhor linhagem produtora (6,10 ± 0,30 U/mL), sendo o farelo de trigo estabelecido como o melhor substrato indutor de sua produção (7,32 ± 0,14 U/mL). Penicillium verruculosum exibiu os maiores níveis de atividade poligalacturonásica (8,65 ± 0,12 U/mL), especialmente quando cultivado em presença de casca de laranja (10,32 ± 0,10 U/mL). O emprego destes resíduos no processo de produção destas enzimas poderá não apenas reduzir seus custos de produção, como também diminuir, substancialmente, o impacto ambiental causado pela deposição destes resíduos no ambiente

Composite Power System Reliability Evaluation Considering Stochastic Parameters Uncertainties

In electrical power systems, the impact of interruptions due to failures can be reduced through expansion planning studies. While high investments result in very expensive and more reliable decisions, reduced investments can lead to unreliable systems. Therefore, it is evident that economic and reliability constraints are conflicting, which makes decision-making difficult in planning and operation stage. The reliability theory, based on probabilities and stochastic processes, allows modeling the random behavior of equipment to estimate performance indices such as Loss of Load Cost. However, parameters as equipment failure rate and repair time are subject to random variations due to limited or nonexistent operating histories, aging and statistical errors. This paper proposes a technique for considering uncertainties on stochastic equipment data in power systems expansion planning. Based on the Monte Carlo Simulation, the proposed technique uses Interval Arithmetic as a method for calculating uncertainty through the theory of imprecise probabilities (P-Box). The application in a test system and a real transmission system allows observing the behavior of the reliability cost as well as the final cost of alternatives for expansion of these systems with the consideration of uncertainties along the expansion horizon

Composite Power System Reliability Evaluation Considering Stochastic Parameters Uncertainties

In electrical power systems, the impact of interruptions due to failures can be reduced through expansion planning studies. While high investments result in very expensive and more reliable decisions, reduced investments can lead to unreliable systems. Therefore, it is evident that economic and reliability constraints are conflicting, which makes decision-making difficult in planning and operation stage. The reliability theory, based on probabilities and stochastic processes, allows modeling the random behavior of equipment to estimate performance indices such as Loss of Load Cost. However, parameters as equipment failure rate and repair time are subject to random variations due to limited or nonexistent operating histories, aging and statistical errors. This paper proposes a technique for considering uncertainties on stochastic equipment data in power systems expansion planning. Based on the Monte Carlo Simulation, the proposed technique uses Interval Arithmetic as a method for calculating uncertainty through the theory of imprecise probabilities (P-Box). The application in a test system and a real transmission system allows observing the behavior of the reliability cost as well as the final cost of alternatives for expansion of these systems with the consideration of uncertainties along the expansion horizon

Composite Power System Reliability Evaluation Considering Stochastic Parameters Uncertainties

In electrical power systems, the impact of interruptions due to failures can be reduced through expansion planning studies. While high investments result in very expensive and more reliable decisions, reduced investments can lead to unreliable systems. Therefore, it is evident that economic and reliability constraints are conflicting, which makes decision-making difficult in planning and operation stage. The reliability theory, based on probabilities and stochastic processes, allows modeling the random behavior of equipment to estimate performance indices such as Loss of Load Cost. However, parameters as equipment failure rate and repair time are subject to random variations due to limited or nonexistent operating histories, aging and statistical errors. This paper proposes a technique for considering uncertainties on stochastic equipment data in power systems expansion planning. Based on the Monte Carlo Simulation, the proposed technique uses Interval Arithmetic as a method for calculating uncertainty through the theory of imprecise probabilities (P-Box). The application in a test system and a real transmission system allows observing the behavior of the reliability cost as well as the final cost of alternatives for expansion of these systems with the consideration of uncertainties along the expansion horizon