Duration of larval and pupal development stages of Aedes albopictus in natural and artificial containers

Aedes albopictus were reared in different containers: a tree hole, a bamboo stump and an auto tire. The total times from egg hatching to adult emergence were of 19.6,27.3 and 37.5 days, respectively, according to the container. The first, second and third-instar larvae presented growth periods with highly similar durations. The fourth-instar larvae was longer than the others stages. The pupation time was longer than the fourth-instar larvae growth period. The temperature of the breeding sites studied, which was of 18° C to 22° C on average, was also taken into consideration. The mortality of the immature stages was analysed and compared as between the experimental groups; it was lower in the natural containers than in the discarded tire. The average wing length of adult females emerging from tree hole was significantly larger (p < 0.05) than that of those emerging from the tire.

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.

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.

Mixed integer non linear programming and artificial neural network based approach to ancillary services dispatch in competitive electricity markets

Ancillary services represent a good business opportunity that must be considered by market players. This paper presents a new methodology for ancillary services market dispatch. The method considers the bids submitted to the market and includes a market clearing mechanism based on deterministic optimization. An Artificial Neural Network is used for day-ahead prediction of Regulation Down, regulation-up, Spin Reserve and Non-Spin Reserve requirements. Two test cases based on California Independent System Operator data concerning dispatch of Regulation Down, Regulation Up, Spin Reserve and Non-Spin Reserve services are included in this paper to illustrate the application of the proposed method: (1) dispatch considering simple bids; (2) dispatch considering complex bids.

Trypanosoma cruzi parasitemia observed in immunocompromised patients: the importance of the artificial xenodiagnosis

Trypanosoma cruzi parasitemia observed in immunocompromised patients (transplant or positive HIV) occurred more frequently by the artificial xenodiagnosis method (10/38) compared with hemoculture (2/38), given the same quantity of blood. Other ways of diagnosis, like mice inoculation (5/38), QBC and buffy coat (2/38), were evaluated also. This result showed the importance of the artificial xenodiagnosis. The other techniques increased only one more patient positive.

A flexible navigation system for autonomous robots operating in heterogeneous environments

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores

Rapid assessment of fruit-color selection by birds using artificial fruits at local scale in Central Amazonia

Modeling clays have been used in several ecological experiments and have proved to be an important tool to variables control. The objective of our study was to determine if fruit color in isolated and grouped displays influences the fruit selection by birds in the field using artificial fruits. Data were collected in six plots distributed homogeneously in 3 km long trails with a minimum distance of 0.5 km. We used a paired experimental design to establish our experiments, so that all treatments were available to the local bird community in each plot. Overall, red was more pecked than brown and white. Isolated red and brown displays were significantly more pecked than others display. Even though our study was conducted in small spatial scales, artificial fruits appeared to be efficient in register fruit consumption attempts by bird. Although inconclusive about selective forces that sharp the dynamics of fruit color polymorphisms and choice by frugivorous birds, our findings corroborate recent studies wherein birds showed preferences by high- over low-contrast fruit signals.

Abstract computation in schizophrenia detection through artificial neural network based systems

Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six) months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason) of defective information.

Artificial neural networks in diagnosis of liver diseases

Liver diseases have severe patients’ consequences, being one of the main causes of premature death. These facts reveal the centrality of one`s daily habits, and how important it is the early diagnosis of these kind of illnesses, not only to the patients themselves, but also to the society in general. Therefore, this work will focus on the development of a diagnosis support system to these kind of maladies, built under a formal framework based on Logic Programming, in terms of its knowledge representation and reasoning procedures, complemented with an approach to computing grounded on Artificial Neural Networks.

Logic programming and artificial neural networks in breast cancer detection

About 90% of breast cancers do not cause or are capable of producing death if detected at an early stage and treated properly. Indeed, it is still not known a specific cause for the illness. It may be not only a beginning, but also a set of associations that will determine the onset of the disease. Undeniably, there are some factors that seem to be associated with the boosted risk of the malady. Pondering the present study, different breast cancer risk assessment models where considered. It is our intention to develop a hybrid decision support system under a formal framework based on Logic Programming for knowledge representation and reasoning, complemented with an approach to computing centered on Artificial Neural Networks, to evaluate the risk of developing breast cancer and the respective Degree-of-Confidence that one has on such a happening.

Artificial nest predation rates vary depending on visibility in the eastern Brazilian Amazon

Observational and experimental studies have shown that increased concealment of bird nests reduces nest predation rates. The objective of the present study was to evaluate differences in predation rates between two experimental manipulations of artificial ground nests (i.e., clearing an area around the artificial nest or leaving it as natural as possible), and test whether environmental variables also affected nest predation in an undisturbed area of Amazonian forest in eastern Brazil. A generalized linear model was used to examine the influence of five variables (manipulation type, perpendicular distance from the main trail, total basal area of trees surrounding the nest site, understorey density, and liana quantity) on nest predation rates. Model results, showed that manipulation type was the only variable that significantly affected nest predation rates. Thus, to avoid systematic biases, the influence of nest site manipulation must be taken into consideration when conducting experiments with artificial nests.

Results of computer assisted navigation in Total Knee Arthroplasty (TKA): comparison with the conventional method

The aim of this retrospective study was to compare the clinical and radiographic results after TKA (PFC, DePuy), performed either by computer assisted navigation (CAS, Brainlab, Johnson&Johnson) or by conventional means. Material and methods: Between May and December 2006 we reviewed 36 conventional TKA performed between 2002 and 2003 (group A) and 37 navigated TKA performed between 2005 and 2006 (group B) by the same experienced surgeon. The mean age in group A was 74 years (range 62-90) and 73 (range 58-85) in group B with a similar age distribution. The preoperative mechanical axes in group A ranged from -13° varus to +13° valgus (mean absolute deviation 6.83°, SD 3.86), in group B from -13° to +16° (mean absolute deviation 5.35, SD 4.29). Patients with a previous tibial osteotomy or revision arthroplasty were excluded from the study. Examination was done by an experienced orthopedic resident independent of the surgeon. All patients had pre- and postoperative long standing radiographs. The IKSS and the WOMAC were utilized to determine the clinical outcome. Patient's degree of satisfaction was assessed on a visual analogous scale (VAS). Results: 32 of the 37 navigated TKAs (86,5%) showed a postoperative mechanical axis within the limits of 3 degrees of valgus or varus deviation compared to only 24 (66%) of the 36 standard TKAs. This difference was significant (p = 0.045). The mean absolute deviation from neutral axis was 3.00° (range -5° to +9°, SD: 1.75) in group A in comparison to 1.54° (range -5° to +4°, SD: 1.41) in group B with a highly significant difference (p = 0.000). Furthermore, both groups showed a significant postoperative improvement of their mean IKSS-values (group A: 89 preoperative to 169 postoperative, group B 88 to 176) without a significant difference between the two groups. Neither the WOMAC nor the patient's degree of satisfaction - as assessed by VAS - showed significant differences. Operation time was significantly higher in group B (mean 119.9 min.) than in group A (mean 99.6 min., p <0.000). Conclusion: Our study showed consistent significant improvement of postoperative frontal alignment in TKA by computer assisted navigation (CAS) compared to standard methods, even in the hands of a surgeon well experienced in standard TKA implantation. However, the follow-up time of this study was not long enough to judge differences in clinical outcome. Thus, the relevance of computer navigation for clinical outcome and survival of TKA remains to be proved in long term studies to justify the longer operation time. References 1 Stulberg SD. Clin Orth Rel Res. 2003;(416):177-84. 2 Chauhan SK. JBJS Br. 2004;86(3):372-7. 3 Bäthis H, et al. Orthopäde. 2006;35(10):1056-65.

Transmission immunity in malaria: reflections on the underlying immune mechanisms during natural infections and following artificial immunization

Malaria transmission-blocking immunity has been studied in natural malaria infections in man, during infections in animals and following artificial immunization of animals with sexual stage malaria parasites. Effective immunity, which prevents infectivity of a malarial infection to mosquitoes, has been observed under all of these circumstances. Two general types of effector mechanism have been identified. One is an antibody mediated mechanism which acts against the extracellular sexual stages of the parasite within the midgut of a blood feeding mosquito. The other is a cytokine mediated mechanism which inactivates the gametocytes of the parasites while in the circulation of the vertebrate host. Both effects have been observed during natural infections and following artificial immunization. The basis of induction of transmission-blocking immunity, including the nature of the memory for such immunity, however, may be very different in different host/parasite systems and during natural infection of following artificial immunization. Following artificial immunization a strong immune memory for transmission blocking immunity has been observed in animal systems. By contrast, following natural infections in man immune memory for transmission blocking immunity has been found to be weak and short lived if it occurs at all. It is suggested that the immunogens which induce natural transmission blocking immunity may be CD4+ independent.

Artificial Snow Optimization in Winter Sport Destinations Using a Multi-agent Simulation

This paper presents the Juste-Neige system for predicting the snow height on the ski runs of a resort using a multi-agent simulation software. Its aim is to facilitate snow cover management in order to i) reduce the production cost of artificial snow and to improve the profit margin for the companies managing the ski resorts; and ii) to reduce the water and energy consumption, and thus to reduce the environmental impact, by producing only the snow needed for a good skiing experience. The software provides maps with the predicted snow heights for up to 13 days. On these maps, the areas most exposed to snow erosion are highlighted. The software proceeds in three steps: i) interpolation of snow height measurements with a neural network; ii) local meteorological forecasts for every ski resort; iii) simulation of the impact caused by skiers using a multi-agent system. The software has been evaluated in the Swiss ski resort of Verbier and provides useful predictions.