Aerial surveys to monitor bluefin tuna abundance and track efficiency of management measures

Conservation and management measures for exploited fish species rely on our ability to monitor variations in population abundance. In the case of the eastern stock of Atlantic bluefin tuna (ABFT), recent changes in management policies have strongly affected the reliability of fishery-dependent indicators due to drastic changes in fishing season/area, fisheries selectivity and strategy. However, fishery-independent indices of abundance are rare for large pelagic fish, and obtaining them is often costly and labor intensive. Here, we show that scientific aerial surveys are an appropriate tool for monitoring juvenile bluefin tuna abundance in the Mediterranean. We present an abundance index based on 62 aerial surveys conducted since 2000, using 2 statistical approaches to deal with the sampling strategy: line and strip transects. Both approaches showed a significant increase in juvenile ABFT abundance in recent years, resulting from the recovery plan established in 2007. Nonetheless, the estimates from the line transect method appear to be more robust and stable. This study provides essential information for fisheries management. Expanding the spatial coverage to other nursery grounds would further increase the reliability and representativeness of this index.

Structured decision making as a tool for facilitating stakeholder engagement in non-renewable resource management: using the case study of hydraulic fracturing in the Green Point Shale formation in western Newfoundland

This thesis examines the importance of effective stakeholder engagement that complies with the doctrines of social justice in non-renewable resources management decision-making. It uses hydraulic fracturing in the Green Point Shale Formation in Western Newfoundland as a case study. The thesis uses as theoretical background John Rawls’ and David Miller’ theory of social justice, and identifies the social justice principles, which are relevant to stakeholder engagement. The thesis compares the method of stakeholder engagement employed by the Newfoundland and Labrador Hydraulic Fracturing Review Panel (NLHFRP), with the stakeholder engagement techniques recommended by the Structured Decision Making (SDM) model, as applied to a simulated case study involving hydraulic fracturing in the Green Point Shale Formation. Using the already identified social justice principles, the thesis then developed a framework to measure the level of compliance of both stakeholder engagement techniques with social justice principles. The main finding of the thesis is that the engagement techniques prescribed by the SDM model comply more closely with the doctrines of social justice than the engagement techniques applied by the NLHFRP. The thesis concludes by recommending that the SDM model be more widely used in non- renewable resource management decision making in order to ensure that all stakeholders’ concerns are effectively heard, understood and transparently incorporated in the nonrenewable resource policies to make them consistent with local priorities and goals, and with the social justice norms and institutions.

Pressure ulcer management in Oman: nurses’ knowledge and views

Background: Pressure ulcers (PrUs) have a significant impact on health system expenditure and patient’s quality of life. It is a global problem. Many studies were undertaken in regard to PrU prevention and management. In Oman, no studies have been conducted to investigate nurses’ knowledge on prevention and management of PrUs. The purpose of this descriptive sequential explanatory mixed-method study was to explore the nurses’ level of knowledge in relation to prevention and management of PrUs in Oman. Methods: A mixed method design was used and the study was conducted over two Phases. In Phase I, a questionnaire was developed to explore nurses’ knowledge on PrU, policy, and resources. The main section of the questionnaire was the Pieper-Zulkowski Pressure Ulcer knowledge test (PZ-PUKT) which tests the knowledge on PrU. Another two sections were developed including questions about wound policy and resources available for PrU prevention and management in Oman. The questionnaire was distributed to nurses who were working in surgical, medical, orthopaedic, CCU, and ICU wards/units in seven hospitals. In Phase II study, semi-structured qualitative interviews were conducted with 16 of the questionnaire respondents. Interviews took approximately 30 minutes, were recorded and transcribed verbatim. Qualitative data were analysed using the Knowledge, Attitudes and Practice (KAP) model as the a priori framework. Results: In Phase I, 478 questionnaires were analysed. The knowledge test results showed the overall mean percent score for correctly answered questions was 51% suggesting a low level of knowledge. There was a significant relationship between nurses’ knowledge and age (P=0.001) and between knowledge and years of experience (P=0.001) with knowledge increasing with age and years of experience. In Phase II, four themes were identified from the interviews: knowledge, attitude, and practice (framework themes) and perception of role. Findings indicated positive and negative attitudes towards the care of PrUs. Some nurses stated feeling rewarded when they see wounds improving while others said they could not work with patients independently because they lacked the knowledge and the skills needed. There was variation in the management of PrU between hospitals. Both studies indicated that the wound management policy did not include enough information to guide nurses. Conclusion: Overall the nurses’ level of knowledge on PrU was relatively low. Most nurses were not familiar with wound management policy or different PrU prevention and management strategies. Nurses are aware of the risk of PrUs and try their best to manage them with the available resources however more training is required.

Assessing earnings management in european publicly traded firms

We investigate if investors may benefit from using the accruals quality measure to assess the level of earnings management exercised by firms when preparing their accounting statements. More earnings management is expected to be associated with high information asymmetry among stock market participants because it makes earnings information less precise, thus providing an information advantage to informed investors relative to liquidity traders. Our results based on a sample of European publicly traded firms are consistent with a positive association between earnings management and information asymmetry. However, given some previous studies suggesting that accruals based measures may be noisy indicators of earnings management we further develop and test a method to enhance the performance of accruals quality in detecting earnings management.

Assessing Earnings Management in European Publicly Traded Firms

We investigate if investors may benefit from using the accruals quality measure to assess the level of earnings management exercised by firms when preparing their accounting statements. More earnings management is expected to be associated with high information asymmetry among stock market participants because it makes earnings information less precise, thus providing an information advantage to informed investors relative to liquidity traders. Our results based on a sample of European publicly traded firms are consistent with a positive association between earnings management and information asymmetry. However, given some previous studies suggesting that accruals based measures may be noisy indicators of earnings management we further develop and test a method to enhance the performance of accruals quality in detecting earnings management

French good practice guidelines for management of the risk of low back pain among workers exposed to manual material handling: Hierarchical strategy of risk assessment of work situations

International audience

Trajectory prediction uncertainty modelling for Air Traffic Management

The anticipated growth of air traffic worldwide requires enhanced Air Traffic Management (ATM) technologies and procedures to increase the system capacity, efficiency, and resilience, while reducing environmental impact and maintaining operational safety. To deal with these challenges, new automation and information exchange capabilities are being developed through different modernisation initiatives toward a new global operational concept called Trajectory Based Operations (TBO), in which aircraft trajectory information becomes the cornerstone of advanced ATM applications. This transformation will lead to higher levels of system complexity requiring enhanced Decision Support Tools (DST) to aid humans in the decision making processes. These will rely on accurate predicted aircraft trajectories, provided by advanced Trajectory Predictors (TP). The trajectory prediction process is subject to stochastic effects that introduce uncertainty into the predictions. Regardless of the assumptions that define the aircraft motion model underpinning the TP, deviations between predicted and actual trajectories are unavoidable. This thesis proposes an innovative method to characterise the uncertainty associated with a trajectory prediction based on the mathematical theory of Polynomial Chaos Expansions (PCE). Assuming univariate PCEs of the trajectory prediction inputs, the method describes how to generate multivariate PCEs of the prediction outputs that quantify their associated uncertainty. Arbitrary PCE (aPCE) was chosen because it allows a higher degree of flexibility to model input uncertainty. The obtained polynomial description can be used in subsequent prediction sensitivity analyses thanks to the relationship between polynomial coefficients and Sobol indices. The Sobol indices enable ranking the input parameters according to their influence on trajectory prediction uncertainty. The applicability of the aPCE-based uncertainty quantification detailed herein is analysed through a study case. This study case represents a typical aircraft trajectory prediction problem in ATM, in which uncertain parameters regarding aircraft performance, aircraft intent description, weather forecast, and initial conditions are considered simultaneously. Numerical results are compared to those obtained from a Monte Carlo simulation, demonstrating the advantages of the proposed method. The thesis includes two examples of DSTs (Demand and Capacity Balancing tool, and Arrival Manager) to illustrate the potential benefits of exploiting the proposed uncertainty quantification method.

An ontology for risk management of digital collections

Maintaining accessibility to and understanding of digital information over time is a complex challenge that often requires contributions and interventions from a variety of individuals and organizations. The processes of preservation planning and evaluation are fundamentally implicit and share similar complexity. Both demand comprehensive knowledge and understanding of every aspect of to-be-preserved content and the contexts within which preservation is undertaken. Consequently, means are required for the identification, documentation and association of those properties of data, representation and management mechanisms that in combination lend value, facilitate interaction and influence the preservation process. These properties may be almost limitless in terms of diversity, but are integral to the establishment of classes of risk exposure, and the planning and deployment of appropriate preservation strategies. We explore several research objectives within the course of this thesis. Our main objective is the conception of an ontology for risk management of digital collections. Incorporated within this are our aims to survey the contexts within which preservation has been undertaken successfully, the development of an appropriate methodology for risk management, the evaluation of existing preservation evaluation approaches and metrics, the structuring of best practice knowledge and lastly the demonstration of a range of tools that utilise our findings. We describe a mixed methodology that uses interview and survey, extensive content analysis, practical case study and iterative software and ontology development. We build on a robust foundation, the development of the Digital Repository Audit Method Based on Risk Assessment. We summarise the extent of the challenge facing the digital preservation community (and by extension users and creators of digital materials from many disciplines and operational contexts) and present the case for a comprehensive and extensible knowledge base of best practice. These challenges are manifested in the scale of data growth, the increasing complexity and the increasing onus on communities with no formal training to offer assurances of data management and sustainability. These collectively imply a challenge that demands an intuitive and adaptable means of evaluating digital preservation efforts. The need for individuals and organisations to validate the legitimacy of their own efforts is particularly prioritised. We introduce our approach, based on risk management. Risk is an expression of the likelihood of a negative outcome, and an expression of the impact of such an occurrence. We describe how risk management may be considered synonymous with preservation activity, a persistent effort to negate the dangers posed to information availability, usability and sustainability. Risk can be characterised according to associated goals, activities, responsibilities and policies in terms of both their manifestation and mitigation. They have the capacity to be deconstructed into their atomic units and responsibility for their resolution delegated appropriately. We continue to describe how the manifestation of risks typically spans an entire organisational environment, and as the focus of our analysis risk safeguards against omissions that may occur when pursuing functional, departmental or role-based assessment. We discuss the importance of relating risk-factors, through the risks themselves or associated system elements. To do so will yield the preservation best-practice knowledge base that is conspicuously lacking within the international digital preservation community. We present as research outcomes an encapsulation of preservation practice (and explicitly defined best practice) as a series of case studies, in turn distilled into atomic, related information elements. We conduct our analyses in the formal evaluation of memory institutions in the UK, US and continental Europe. Furthermore we showcase a series of applications that use the fruits of this research as their intellectual foundation. Finally we document our results in a range of technical reports and conference and journal articles. We present evidence of preservation approaches and infrastructures from a series of case studies conducted in a range of international preservation environments. We then aggregate this into a linked data structure entitled PORRO, an ontology relating preservation repository, object and risk characteristics, intended to support preservation decision making and evaluation. The methodology leading to this ontology is outlined, and lessons are exposed by revisiting legacy studies and exposing the resource and associated applications to evaluation by the digital preservation community.

Knowledge and use of asthma control measurement tools in the management of asthma: a survey of doctors working in family and internal medicine practice in Nigeria.

Objective: To investigate the knowledge and use of asthma control measurement (ACM) tools in the management of asthma among doctors working in family and internal medicine practice in Nigeria. Method: A questionnaire based on the global initiative on asthma (GINA) guideline was self-administered by 194 doctors. It contains 12 test items on knowledge of ACM tools and its application. The knowledge score was obtained by adding the correct answers and classified as good if the score ≥ 9, satisfactory if score was 6-8 and poor if < 6. Results: The overall doctors knowledge score of ACM tools was 4.49±2.14 (maximum of 12). Pulmonologists recorded the highest knowledge score of 10.75±1.85. The majority (69.6%) had poor knowledge score of ACM tools. Fifty (25.8%) assessed their patients’ level of asthma control and 34(17.5%) at every visit. Thirty-nine (20.1%) used ACM tools in their consultation, 29 (15.0%) of them used GINA defined control while 10 (5.2 %) used asthma control test (ACT). The use of the tools was associated with pulmonologists, having attended CME within six months and graduated within five years prior to the survey. Conclusion: The results highlight the poor knowledge and use of ACM tools and the need to address the knowledge gap.

UNDERSTANDING CUSTOMER CHOICES IN SERVICE OUTSOURCING AND REVENUE MANAGEMENT

This dissertation investigates customer behavior modeling in service outsourcing and revenue management in the service sector (i.e., airline and hotel industries). In particular, it focuses on a common theme of improving firms’ strategic decisions through the understanding of customer preferences. Decisions concerning degrees of outsourcing, such as firms’ capacity choices, are important to performance outcomes. These choices are especially important in high-customer-contact services (e.g., airline industry) because of the characteristics of services: simultaneity of consumption and production, and intangibility and perishability of the offering. Essay 1 estimates how outsourcing affects customer choices and market share in the airline industry, and consequently the revenue implications from outsourcing. However, outsourcing decisions are typically endogenous. A firm may choose whether to outsource or not based on what a firm expects to be the best outcome. Essay 2 contributes to the literature by proposing a structural model which could capture a firm’s profit-maximizing decision-making behavior in a market. This makes possible the prediction of consequences (i.e., performance outcomes) of future strategic moves. Another emerging area in service operations management is revenue management. Choice-based revenue systems incorporate discrete choice models into traditional revenue management algorithms. To successfully implement a choice-based revenue system, it is necessary to estimate customer preferences as a valid input to optimization algorithms. The third essay investigates how to estimate customer preferences when part of the market is consistently unobserved. This issue is especially prominent in choice-based revenue management systems. Normally a firm only has its own observed purchases, while those customers who purchase from competitors or do not make purchases are unobserved. Most current estimation procedures depend on unrealistic assumptions about customer arriving. This study proposes a new estimation methodology, which does not require any prior knowledge about the customer arrival process and allows for arbitrary demand distributions. Compared with previous methods, this model performs superior when the true demand is highly variable.

Cow, farm, and herd management factors in the dry period associated with raised somatic cell counts in early lactation

This study investigated cow characteristics, farm facilities, and herd management strategies during the dry period to examine their joint influence on somatic cell counts (SCC) in early lactation. Data from 52 commercial dairy farms throughout England and Wales were collected over a 2-yr period. For the purpose of analysis, cows were separated into those housed for the dry period (6,419 cow-dry periods) and those at pasture (7,425 cow-dry periods). Bayesian multilevel models were specified with 2 response variables: ln SCC (continuous) and SCC >199,000 cells/mL (binary), both within 30 d of calving. Cow factors associated with an increased SCC after calving were parity, an SCC >199,000 cells/mL in the 60 d before drying off, increasing milk yield 0 to 30 d before drying off, and reduced DIM after calving at the time of SCC estimation. Herd management factors associated with an increased SCC after calving included procedures at drying off, aspects of bedding management, stocking density, and method of pasture grazing. Posterior predictions were used for model assessment, and these indicated that model fit was generally good. The research demonstrated that specific dry-period management strategies have an important influence on SCC in early lactation.

Active versus passive management : the case of BOVESPA

Mestrado em Finanças

Integrated pest management and the (un)sustainable use of pesticides

Doutoramento em Engenharia Agronómica - Instituto Superior de Agronomia - UL

APPLICATION OF AN IMPUTATION METHOD FOR GEOSPATIAL INVENTORY OF FOREST STRUCTURAL ATTRIBUTES ACROSS MULTIPLE SPATIAL SCALES IN THE LAKE STATES, U.S.A.

Credible spatial information characterizing the structure and site quality of forests is critical to sustainable forest management and planning, especially given the increasing demands and threats to forest products and services. Forest managers and planners are required to evaluate forest conditions over a broad range of scales, contingent on operational or reporting requirements. Traditionally, forest inventory estimates are generated via a design-based approach that involves generalizing sample plot measurements to characterize an unknown population across a larger area of interest. However, field plot measurements are costly and as a consequence spatial coverage is limited. Remote sensing technologies have shown remarkable success in augmenting limited sample plot data to generate stand- and landscape-level spatial predictions of forest inventory attributes. Further enhancement of forest inventory approaches that couple field measurements with cutting edge remotely sensed and geospatial datasets are essential to sustainable forest management. We evaluated a novel Random Forest based k Nearest Neighbors (RF-kNN) imputation approach to couple remote sensing and geospatial data with field inventory collected by different sampling methods to generate forest inventory information across large spatial extents. The forest inventory data collected by the FIA program of US Forest Service was integrated with optical remote sensing and other geospatial datasets to produce biomass distribution maps for a part of the Lake States and species-specific site index maps for the entire Lake State. Targeting small-area application of the state-of-art remote sensing, LiDAR (light detection and ranging) data was integrated with the field data collected by an inexpensive method, called variable plot sampling, in the Ford Forest of Michigan Tech to derive standing volume map in a cost-effective way. The outputs of the RF-kNN imputation were compared with independent validation datasets and extant map products based on different sampling and modeling strategies. The RF-kNN modeling approach was found to be very effective, especially for large-area estimation, and produced results statistically equivalent to the field observations or the estimates derived from secondary data sources. The models are useful to resource managers for operational and strategic purposes.

Development of an innovative non rule-based energy management strategy for a Hybrid Electric Vehicle, structured for predictive driving controls based on external information knowledge

Recently, the interest of the automotive market for hybrid vehicles has increased due to the more restrictive pollutants emissions legislation and to the necessity of decreasing the fossil fuel consumption, since such solution allows a consistent improvement of the vehicle global efficiency. The term hybridization regards the energy flow in the powertrain of a vehicle: a standard vehicle has, usually, only one energy source and one energy tank; instead, a hybrid vehicle has at least two energy sources. In most cases, the prime mover is an internal combustion engine (ICE) while the auxiliary energy source can be mechanical, electrical, pneumatic or hydraulic. It is expected from the control unit of a hybrid vehicle the use of the ICE in high efficiency working zones and to shut it down when it is more convenient, while using the EMG at partial loads and as a fast torque response during transients. However, the battery state of charge may represent a limitation for such a strategy. That’s the reason why, in most cases, energy management strategies are based on the State Of Charge, or SOC, control. Several studies have been conducted on this topic and many different approaches have been illustrated. The purpose of this dissertation is to develop an online (usable on-board) control strategy in which the operating modes are defined using an instantaneous optimization method that minimizes the equivalent fuel consumption of a hybrid electric vehicle. The equivalent fuel consumption is calculated by taking into account the total energy used by the hybrid powertrain during the propulsion phases. The first section presents the hybrid vehicles characteristics. The second chapter describes the global model, with a particular focus on the energy management strategies usable for the supervisory control of such a powertrain. The third chapter shows the performance of the implemented controller on a NEDC cycle compared with the one obtained with the original control strategy.