Estimated Mortality of Selected Migratory Bird Species from Mowing and Other Mechanical Operations in Canadian Agriculture

Mechanical operations such as mowing, tilling, seeding, and harvesting are well-known sources of direct avian mortality in agricultural fields. However, there are currently no mortality rate estimates available for any species group or larger jurisdiction. Even reviews of sources of mortality in birds have failed to address mechanical disturbance in farm fields. To overcome this information gap we provide estimates of total mortality rates by mechanical operations for five selected species across Canada. In our step-by-step modeling approach we (i) quantified the amount of various types of agricultural land in each Bird Conservation Region (BCR) in Canada, (ii) estimated population densities by region and agricultural habitat type for each selected species, (iii) estimated the average timing of mechanical agricultural activities, egg laying, and fledging, (iv) and used these values and additional demographical parameters to derive estimates of total mortality by species within each BCR. Based on our calculations the total annual estimated incidental take of young ranged from ~138,000 for Horned Lark (Eremophila alpestris) to as much as ~941,000 for Savannah Sparrow (Passerculus sandwichensis). Net losses to the fall flight of birds, i.e., those birds that would have fledged successfully in the absence of mechanical disturbance, were, for example ~321,000 for Bobolink (Dolichonyx oryzivorus) and ~483,000 for Savannah Sparrow. Although our estimates are subject to an unknown degree of uncertainty, this assessment is a very important first step because it provides a broad estimate of incidental take for a set of species that may be particularly vulnerable to mechanical operations and a starting point for future refinements of model parameters if and when they become available.

Rationality of service composition of Workflow Net in a service oriented architecture

Users’ requirements change drives an information system evolution. Consequently, such evolution affects those atomic services which provide functional operations from one state of their composition to another state of composition. A challenging issue associated with such evolution of the state of service composition is to ensure a resultant service composition remaining rational. This paper presents a method of Service Composition Atomic-Operation Set (SCAOS). SCAOS defines 2 classes of atomic operations and 13 kinds of basic service compositions to aid a state change process by using Workflow Net. The workflow net has algorithmic capabilities to compose the required services with rationality and maintain any changes to the services in a different composition also rational. This method can improve the adaptability to the ever changing business requirements of information systems in the dynamic environment.

Elementary Petri net inside RFID distributed database (PNRD)

Usually, a Petri net is applied as an RFID model tool. This paper, otherwise, presents another approach to the Petri net concerning RFID systems. This approach, called elementary Petri net inside an RFID distributed database, or PNRD, is the first step to improve RFID and control systems integration, based on a formal data structure to identify and update the product state in real-time process execution, allowing automatic discovery of unexpected events during tag data capture. There are two main features in this approach: to use RFID tags as the object process expected database and last product state identification; and to apply Petri net analysis to automatically update the last product state registry during reader data capture. RFID reader data capture can be viewed, in Petri nets, as a direct analysis of locality for a specific transition that holds in a specific workflow. Following this direction, RFID readers storage Petri net control vector list related to each tag id is expected to be perceived. This paper presents PNRD cornerstones and a PNRD implementation example in software called DEMIS Distributed Environment in Manufacturing Information Systems.

Operations strategy in perspective: the means for achieving superior performance

Manufacturing strategy has been widely studied and it is increasingly gaining attention. It has a fundamental role that is to translate the business strategy to the operations by developing the capabilities that are needed by the company in order to accomplish the desired performance. More precisely, manufacturing strategy comprises the decisions that managers take during a certain period of time in order to achieve a desire result. These decisions are related to which operational practices and resources are implemented. Our goal was to identify the relationship between these two decisions with operational performance. We based our arguments on the resource-based view for identifying sources of competitive advantage. Hence, we argued that operational practices and resources affect positively the operational performances. Additionally, we proposed that in the presence of some resources the implementation of operational practices would lead to a greater performance. We used previous scales for measuring operational practices and performance, and developed new constructs for resources. The data used is part of the High Performance Manufacturing project and the sample is composed by 291 plants. Through confirmatory factor analysis and multiple regressions we found that operational practices to a certain extant are positively related to operational performance. More specifically, the results show that JIT and customer orientation practices have a positive relationship with quality, delivery, flexibility, and cost performances. Moreover, we found that resources like technology and people explain a great variance of operational performance.

Arrecadação recorrente, ciclo econômico e on-off operations

Instituto Brasileiro de Economia

Cultural strategies: how do national cultures impact the operations strategy?

Through the assessment of the fourth round of the High Performance Manufacturing (HPM) project and the introduction of Hofstede’s Cultural Classification, the present work aims to deepen the comprehension of the impact of National Cultures on firms’ Operations Strategy. The ANOVA comparisons of four Operations Strategy elements in countries with different industrialization and development backgrounds (e.g. Germany, China, Brazil and South Korea) suggest that while Integrating Leadership and Implementation of Manufacturing Strategy are affected by the cultural levels of Power Distance, Individualism vs. Collectivism and Uncertainty Avoidance, the other two elements of Operations Strategy, Functional Integration and Formal Manufacturing Strategy, show effects of the degree of Individualism vs. Collectivism and Long-Term Orientation. The results of the study are expected to offer new perspectives on the planning and implementation of strategic and operations management for both practitioners and academics. More specifically, the analysis of cross-cultural influence over operations strategy may contribute to a better understanding of how cooperative behavior may lead firms to generate higher rents through the strengths and weaknesses of their relations, particularly in terms of global supply chains.

Structuring a startup's operations in an emerging market

The present work analyzes the establishment of a startup’s operations and the structuring all of the processes required to start up the business, launch the platform and keep it working. The thesis’ main focus can therefore be described as designing and structuring a startup’s operations in an emerging market before and during its global launch. Such business project aims to provide a successful case regarding the creation of a business and its launch into an emerging market, by illustrating a practical example on how to structure the business’ operations within a limited time frame. Moreover, this work will also perform a complete economic analysis of Brazil, thorough analyses of the industries the company is related to, as well as a competitive analysis of the market the venture operates in. Furthermore, an assessment of the venture’s business model and of its first six-month performance will also be included. The thesis’ ultimate goal lies in evaluating the company’s potential of success in the next few years, by highlighting its strengths and criticalities. On top of providing the company’s management with brilliant findings and forecasts about its own business, the present work will represent a reference and a practical roadmap for any entrepreneur willing to establish his operations in Brazil.

Humanitarian operations in Brazil: a review of natural disasters in the last decade

This research used documentary analysis to identify the main natural disasters in Brazil in the last decade (2003 to 2013). Results provided evidence that operations and impacts differ in sudden-onset and slow-onset disasters and that Government is the main player in the Humanitarian Operations in Brazi

Effects of dredging operations on sediment quality: contaminant mobilization in dredged sediments from the Port of Santos, SP, Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Modeling and analysis of artificial neural networks applied in operations research

Artificial neural networks are dynamic systems consisting of highly interconnected and parallel nonlinear processing elements. Systems based on artificial neural networks have high computational rates due to the use of a massive number of these computational elements. Neural networks with feedback connections provide a computing model capable of solving a rich class of optimization problems. In this paper, a modified Hopfield network is developed for solving problems related to operations research. The internal parameters of the network are obtained using the valid-subspace technique. Simulated examples are presented as an illustration of the proposed approach. Copyright (C) 2000 IFAC.

The minimization of open stacks problem: A review of some properties and their use in pre-processing operations

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Artificial immune systems for classification of petroleum well drilling operations

This paper presents two approaches of Artificial Immune System for Pattern Recognition (CLONALG and Parallel AIRS2) to classify automatically the well drilling operation stages. The classification is carried out through the analysis of some mud-logging parameters. In order to validate the performance of AIS techniques, the results were compared with others classification methods: neural network, support vector machine and lazy learning.

Classification of petroleum well drilling operations using Support Vector Machine (SVM)

During the petroleum well drilling operation many mechanical and hydraulic parameters are monitored by an instrumentation system installed in the rig called a mud-logging system. These sensors, distributed in the rig, monitor different operation parameters such as weight on the hook and drillstring rotation. These measurements are known as mud-logging records and allow the online following of all the drilling process with well monitoring purposes. However, in most of the cases, these data are stored without taking advantage of all their potential. On the other hand, to make use of the mud-logging data, an analysis and interpretationt is required. That is not an easy task because of the large volume of information involved. This paper presents a Support Vector Machine (SVM) used to automatically classify the drilling operation stages through the analysis of some mud-logging parameters. In order to validate the results of SVM technique, it was compared to a classification elaborated by a Petroleum Engineering expert. © 2006 IEEE.

A genetic neuro-model reference adaptive controller for petroleum wells drilling operations

Motivated by rising drilling operation costs, the oil industry has shown a trend towards real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated to parameters modeling. One of the drill-bit performance evaluators, the Rate of Penetration (ROP), has been used in the literature as a drilling control parameter. However, the relationships between the operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on the Auto-Regressive with Extra Input Signals model, or ARX model, to accomplish the system identification and on a Genetic Algorithm (GA) to provide a robust control for the ROP. Results of simulations run over a real offshore oil field data, consisted of seven wells drilled with equal diameter bits, are provided. © 2006 IEEE.