Herbicide efficacy on Borreria densiflora control in pre- and post-emergence conditions

The weed Borreria densiflora is a management issue in soybean and sugarcane crops from North and Northeastern Brazil. Knowledge upon chemical control of B. densiflora contributes to the integrated management of this weed species, especially when active ingredient options become reduced due to the selection of herbicide resistant or tolerant weed species. Experiments in pre- and post-emergence of B. densiflora were conducted in greenhouse, in a randomized block design and four replications. In pre-emergence, the dose-response curve methodology was used and 7 herbicides were tested. In post-emergence, 9 herbicides at the recommended rate and 4 herbicide mixtures were tested. For pre and post-emergence conditions, evaluations were conducted at 60 and 21 days after treatment (DAT), respectively, and the variables analyzed were weed control and dry weight (%). The results showed options of pre-emergent herbicides that can be used for controlling B. densiflora, especially in sugarcane, where chemical weed control is mainly based on pre-emergent applications. In the current glyphosate resistance scenario, one should consider the use of pre-emergent herbicides within an integrated management of B. densiflora. For satisfactory post-emergence control, B. densiflora plants should be sprayed at the phenological stage of up to three pairs of leaves. Herbicide mixtures have been and will continue to be an important tool in chemical weed management, broadening the spectrum of weed control, while diversifying herbicide mechanisms of action, which helps to prevent or delay the appearance of herbicide resistance.

Selectivity of chemical weed control systems in conventional cotton

Cotton is highly susceptible to the interference imposed by weed community, being therefore essential to adopt control measures ensuring the crop yield. Herbicides are the primary method of weed control in large-scale areas of production, and usually more than one herbicide application is necessary due to the extensive crop cycle. This study aimed to evaluate the selectivity of different chemical weed control systems for conventional cotton. The experiment took place in the field in a randomized block design, with twenty nine treatments and four replications in a split plot layout (adjacent double check). Results showed that triple mixtures in pre-emergence increased the chance of observing reductions in the cotton yield. To avoid reductions in crop yield, users should proceed to a maximum mixture of two herbicides in pre-emergence, followed by S-metolachlor over the top, followed by one post-emergence mixture application of pyrithiobac-sodium + trifloxysulfuron-sodium.

Glyphosate Effectiveness in the Control of Macrophytes Under a Greenhouse Condition

The aim of this study was to assess the effectiveness of glyphosate, as Rodeo(r) formulation, to control Eichhornia crassipes, Pistia stratiotes, Salvinia molesta, Salvinia herzogii and Urochloa subquadripara, under greenhouse conditions. The doses assessed were (480, 960, 1440, 1920, 2400, 2880, 3360 and 3840 g ha-1 of glyphosate) with 0.5% of the Aterbane(r) BR surfactant and a control, with no herbicide application. All experiments were conducted in a completely randomized experimental design with ten replications. Applications were carried out by precision backpack sprayer at a CO2 constant pressure of 25 psi and spray solution consumption of 200 L ha-1. Product effectiveness was determined by assessing the growth inhibitions and phytotoxicity signals during the periods of 3, 7, 15, 21, 30 and 45 days after application (DAA). At 45 DAA, the highest glyphosate dose (3840 g ha-1) displayed 100% effectiveness for all four macrophyte species. Under greenhouse conditions, the glyphosate in formulation Rodeo(r) with 0.5% of Aterbarne(r) BR surfactant showed excellent effectiveness, inhibiting the growth of the floating aquatic macrophytes. This procedure can be included on the tillage plans for these species.

INTERCROPPING CORN WITH A COMBINATION OF TREE SPECIES TO CONTROL WEEDS

ABSTRACT The combination of crop residues or crop extracts is often more advantageous in controlling weeds, than the application of each residue or extract singly. This suggests that in intercropping with maize, the combination of tree species can be more advantageous than species isolated in weed control. The objective of this study was to evaluate the effects of intercropping with a combination of leguminous on the weed growth and corn yield. A randomized-block design with split plots (cultivars in plots) and five replicates was established. The cultivars BR 205 and AG 1041 were subject to the following treatments: two weedings (A), intercropping with sabiá (B), gliricidia (C), gliricidia + sabiá (D) and no weeding (E). In the B and C, 30 viable seeds m-2 of the leguminous were sown. In the D, 15 seeds of each species were sown m-2. The legumes were sown by random casting during corn planting. The sequence of the best treatments in reducing the growth of weeds is A > B = C = D = E. The sequence of the best treatments when are considered the yields of baby corn, green corn and grain is A > B > C > D > E. The cultivars do not differ in regards to the reduction in weed growth. In terms of corn yield cultivar BR 205 is the best.

Effectiveness of Diquat, Copper Hydroxide, Copper Oxychloride and their Association in Control of Submerged Macrophytes Ceratophyllum demersum

The aims of this study were to evaluate the effectiveness of diquat, copper hydroxide, copper oxychloride and their associations diquat + 0.1% copper oxychloride and diquat + 0.1% copper hydroxide to control Cerathophyllum demersum. Therefore, the concentrations used were 0.1, 0.3, 0.5, 0.7, 1.0 and 1.5 mg L-1 oxychloride and copper hydroxide and 0.2, 0.4, 0.8 and 1.2 mg L-1 diquat and their associations with 0.1% copper oxychloride and 0.1% copper and a control hydroxide. The experimental design was completely randomized with ten replications for 45 days. For evaluation we used a scale of 0-100% control of notes and rated the weight (g) and length (cm) of pointers at the end of the trial period. Diquat showed 100% efficacy at 30 DAA, associations in 21 DAA and copper sources promoted regrowth of C. demersum. Diquat and its associations were more effective in controlling C. demersum. The use of herbicide in combination with a copper source is more efficient for the control of submerged weeds because it potentiates the effect of the herbicide in weed control

Resistence of Radish Biotypes to Iodosulfuron and Alternative Control

The repetitive use of iodosulfuron for the control of weeds in winter cereals in the south of Brazil has favored the emergence of resistant Raphanus sativus biotypes. The objective of this study was to evaluate: the response of Raphanus sativus biotypes susceptible and resistant to different dosages of iodosulfuron; the control of biotypes with alternative registered herbicides for the control of the species in crops of wheat, corn and soybean; and the existence of cross-resistance of the biotypes. Thus, four experiments were done in a greenhouse, with a completely randomized design and four replicates. The experimental units were composed of vases with a volumetric capacity of 0.75 L filled with substrate, containing a plant each. For the dose-response curve, three biotypes (factor A) and nine doses of the iodosulfuron herbicide (factor B) were used. For the alternative control, the recommendation was herbicides in pre or postemergence of the crops, and the crossed-resistance was evaluated by using herbicides that inhibit the ALS enzyme of different chemical groups. The analyzed variables were control and shoot dry matter. GR50 of the susceptible biotype (B1) was 0.11 g a.i. ha-1, whereas GR50 of resistant biotypes (B4 and B13) was 102.9 and 86.8 g a.i. ha-1 of the iodosulfuron herbicide, respectively. The resistant biotypes presented crossed resistance to herbicides that inhibit the ALS enzyme, where the control can be efficient with the use of herbicides with different action mechanisms.

Application Volumes and Sizes of Droplets for the Application of Diquat Herbicide in the Control of Eichhornia crassipes

The objective of this study was to evaluate the efficiency of application and the efficacy of control of water hyacinth (Eichhornia crassipes) with the use of the diquat herbicide done with two application volumes associated to three droplets classes. Three experiments were conducted; in the first, the application efficiency was evaluated; in the second, the control efficiency and; in the third, the droplet spectrum. They were conducted in a completely randomized design with five, four and six replicates, respectively. The treatments were arranged in a 2 x 3 factorial design, with two application volumes (100 and 200 L ha-1) and three droplets classes (fine, extremely coarse and ultra-coarse) to the first and second experiment and the third comprised two diquat concentrations in spray solution (2 and 4 g i.a. L-1) and three droplets classes (fine, extremely coarse and ultra-coarse). The application efficiency was determined by the coverage by droplets, spray deposition and active ingredient of the herbicide (diquat). The efficacy was measured by the control and the percentage of plants with regrowth at 50 days after application. The spectrum of droplets produced per each nozzle model used to obtain the droplets classes were analyzed. According to the parameters analyzed, using the droplets classes extremely thick and ultra thick can provide greater certainty in the application of diquat in the aquatic environment associated with the deposition of the active and sufficient coverage to control Eichhornia crassipes with both application volumes

Cost-efficient virtual machine management : provisioning, admission control, and consolidation

One of the main challenges in Software Engineering is to cope with the transition from an industry based on software as a product to software as a service. The field of Software Engineering should provide the necessary methods and tools to develop and deploy new cost-efficient and scalable digital services. In this thesis, we focus on deployment platforms to ensure cost-efficient scalability of multi-tier web applications and on-demand video transcoding service for different types of load conditions. Infrastructure as a Service (IaaS) clouds provide Virtual Machines (VMs) under the pay-per-use business model. Dynamically provisioning VMs on demand allows service providers to cope with fluctuations on the number of service users. However, VM provisioning must be done carefully, because over-provisioning results in an increased operational cost, while underprovisioning leads to a subpar service. Therefore, our main focus in this thesis is on cost-efficient VM provisioning for multi-tier web applications and on-demand video transcoding. Moreover, to prevent provisioned VMs from becoming overloaded, we augment VM provisioning with an admission control mechanism. Similarly, to ensure efficient use of provisioned VMs, web applications on the under-utilized VMs are consolidated periodically. Thus, the main problem that we address is cost-efficient VM provisioning augmented with server consolidation and admission control on the provisioned VMs. We seek solutions for two types of applications: multi-tier web applications that follow the request-response paradigm and on-demand video transcoding that is based on video streams with soft realtime constraints. Our first contribution is a cost-efficient VM provisioning approach for multi-tier web applications. The proposed approach comprises two subapproaches: a reactive VM provisioning approach called ARVUE and a hybrid reactive-proactive VM provisioning approach called Cost-efficient Resource Allocation for Multiple web applications with Proactive scaling. Our second contribution is a prediction-based VM provisioning approach for on-demand video transcoding in the cloud. Moreover, to prevent virtualized servers from becoming overloaded, the proposed VM provisioning approaches are augmented with admission control approaches. Therefore, our third contribution is a session-based admission control approach for multi-tier web applications called adaptive Admission Control for Virtualized Application Servers. Similarly, the fourth contribution in this thesis is a stream-based admission control and scheduling approach for on-demand video transcoding called Stream-Based Admission Control and Scheduling. Our fifth contribution is a computation and storage trade-o strategy for cost-efficient video transcoding in cloud computing. Finally, the sixth and the last contribution is a web application consolidation approach, which uses Ant Colony System to minimize the under-utilization of the virtualized application servers.

Genetic control of characters associated with bean golden mosaic geminivirus resistance in Phaseolus vulgaris L.

Bean golden mosaic is the most important viral disease of the bean crop (Phaseolus vulgaris L.) in Latin America. The genetics of resistance to a Brazilian strain of bean golden mosaic virus (BGMV), was studied in a 4 x 4 diallel cross without reciprocals, among the parental genotypes DOR 303, EMGOPA 201 Ouro, Carnaval, and Redlands Greenleaf C. Seedlings of the four parents, six F1 hybrids, 12 backcrosses, and F2 generations for each combination were inoculated on the eighth day after sowing by exposure to a viruliferous whitefly (Bemisia tabaci Genn.) population for 24 h, in a glasshouse, prior to transplantation to field conditions. The full set of two parents, F1, F2 and respective backcrosses for each combination was considered to be a family. Data were recorded and analyzed for foliar yellowing, plant dwarfing, and pod malformation, using a randomized block design, with two replications. Weighted generation mean analysis was performed for each of the six families. An additive gene action model was significant for the three characteristics evaluated. On the other hand, non-additive gene action had greater absolute value in most cases. Resistance to foliar yellowing conferred by genes from DRO 303 was highly heritable and was expressed equally well in the different genetic backgrounds evaluated. Such resistance may be oligogenic. Broad- and narrow-sense heritabilities were relatively high for all response traits. The three traits studied were all positively correlated, indicating that they can be simultaneously selected for enhancement. The highest correlation coefficient was obtained for dwarfing x pod malformation.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Mathematical modeling and optimal control of malaria

Malaria continues to infect millions and kill hundreds of thousands of people worldwide each year, despite over a century of research and attempts to control and eliminate this infectious disease. Challenges such as the development and spread of drug resistant malaria parasites, insecticide resistance to mosquitoes, climate change, the presence of individuals with subpatent malaria infections which normally are asymptomatic and behavioral plasticity in the mosquito hinder the prospects of malaria control and elimination. In this thesis, mathematical models of malaria transmission and control that address the role of drug resistance, immunity, iron supplementation and anemia, immigration and visitation, and the presence of asymptomatic carriers in malaria transmission are developed. A within-host mathematical model of severe Plasmodium falciparum malaria is also developed. First, a deterministic mathematical model for transmission of antimalarial drug resistance parasites with superinfection is developed and analyzed. The possibility of increase in the risk of superinfection due to iron supplementation and fortification in malaria endemic areas is discussed. The model results calls upon stakeholders to weigh the pros and cons of iron supplementation to individuals living in malaria endemic regions. Second, a deterministic model of transmission of drug resistant malaria parasites, including the inflow of infective immigrants, is presented and analyzed. The optimal control theory is applied to this model to study the impact of various malaria and vector control strategies, such as screening of immigrants, treatment of drug-sensitive infections, treatment of drug-resistant infections, and the use of insecticide-treated bed nets and indoor spraying of mosquitoes. The results of the model emphasize the importance of using a combination of all four controls tools for effective malaria intervention. Next, a two-age-class mathematical model for malaria transmission with asymptomatic carriers is developed and analyzed. In development of this model, four possible control measures are analyzed: the use of long-lasting treated mosquito nets, indoor residual spraying, screening and treatment of symptomatic, and screening and treatment of asymptomatic individuals. The numerical results show that a disease-free equilibrium can be attained if all four control measures are used. A common pitfall for most epidemiological models is the absence of real data; model-based conclusions have to be drawn based on uncertain parameter values. In this thesis, an approach to study the robustness of optimal control solutions under such parameter uncertainty is presented. Numerical analysis of the optimal control problem in the presence of parameter uncertainty demonstrate the robustness of the optimal control approach that: when a comprehensive control strategy is used the main conclusions of the optimal control remain unchanged, even if inevitable variability remains in the control profiles. The results provide a promising framework for the design of cost-effective strategies for disease control with multiple interventions, even under considerable uncertainty of model parameters. Finally, a separate work modeling the within-host Plasmodium falciparum infection in humans is presented. The developed model allows re-infection of already-infected red blood cells. The model hypothesizes that in severe malaria due to parasite quest for survival and rapid multiplication, the Plasmodium falciparum can be absorbed in the already-infected red blood cells which accelerates the rupture rate and consequently cause anemia. Analysis of the model and parameter identifiability using Markov chain Monte Carlo methods is presented.

Design of Personalization of Massive Open Online Courses

Massive Open Online Courses have been in the center of attention in the recent years. However, the main problem of all online learning environments is their lack of personalization according to the learners’ knowledge, learning styles and other learning preferences. This research explores the parameters and features used for personalization in the literature and based on them, evaluates to see how well the current MOOC platforms have been personalized. Then, proposes a design framework for personalization of MOOC platforms that fulfills most of the personalization parameters in the literature including the learning style as well as personalization features. The result of an assessment made for the proposed design framework shows that the framework well supports personalization of MOOCs.

Cardiovascular control in experimental diabetes

Several studies have reported impairment in cardiovascular function and control in diabetes. The studies cited in this review were carried out from a few days up to 3 months after streptozotocin administration and were concerned with the control of the circulation. We observed that early changes (5 days) in blood pressure control by different peripheral receptors were maintained for several months. Moreover, the impairment of reflex responses observed after baroreceptor and chemoreceptor stimulation was probably related to changes in the efferent limb of the reflex arc (sympathetic and parasympathetic), but changes also in the central nervous system could not be excluded. Changes in renal sympathetic nerve activity during volume expansion were blunted in streptozotocin-treated rats, indicating an adaptive natriuretic and diuretic response in the diabetic state. The improvement of diabetic cardiovascular dysfunction induced by exercise training seems to be related to changes in the autonomic nervous system. Complementary studies about the complex interaction between circulation control systems are clearly needed to adequately address the management of pathophysiological changes associated with diabetes.

Design and Drive-Cycle Based Analysis of Direct-Driven Permanent Magnet Synchronous Machine for a Small Urban Use Electric Vehicle

A direct-driven permanent magnet synchronous machine for a small urban use electric vehicle is presented. The measured performance of the machine at the test bench as well as the performance over the modified New European Drive Cycle will be given. The effect of optimal current components, maximizing the efficiency and taking into account the iron loss, is compared with the simple id=0 – control. The machine currents and losses during the drive cycle are calculated and compared with each other.

Agent-based management systems for many-core platforms : rigorous design and efficient implementation

Due to various advantages such as flexibility, scalability and updatability, software intensive systems are increasingly embedded in everyday life. The constantly growing number of functions executed by these systems requires a high level of performance from the underlying platform. The main approach to incrementing performance has been the increase of operating frequency of a chip. However, this has led to the problem of power dissipation, which has shifted the focus of research to parallel and distributed computing. Parallel many-core platforms can provide the required level of computational power along with low power consumption. On the one hand, this enables parallel execution of highly intensive applications. With their computational power, these platforms are likely to be used in various application domains: from home use electronics (e.g., video processing) to complex critical control systems. On the other hand, the utilization of the resources has to be efficient in terms of performance and power consumption. However, the high level of on-chip integration results in the increase of the probability of various faults and creation of hotspots leading to thermal problems. Additionally, radiation, which is frequent in space but becomes an issue also at the ground level, can cause transient faults. This can eventually induce a faulty execution of applications. Therefore, it is crucial to develop methods that enable efficient as well as resilient execution of applications. The main objective of the thesis is to propose an approach to design agentbased systems for many-core platforms in a rigorous manner. When designing such a system, we explore and integrate various dynamic reconfiguration mechanisms into agents functionality. The use of these mechanisms enhances resilience of the underlying platform whilst maintaining performance at an acceptable level. The design of the system proceeds according to a formal refinement approach which allows us to ensure correct behaviour of the system with respect to postulated properties. To enable analysis of the proposed system in terms of area overhead as well as performance, we explore an approach, where the developed rigorous models are transformed into a high-level implementation language. Specifically, we investigate methods for deriving fault-free implementations from these models into, e.g., a hardware description language, namely VHDL.