Defining a management strategy for the azorean natural thermal resources : valuing criteria identification

Dissertação de Mestrado, Gestão de Empresas (MBA), 23 de Maio de 2016, Universidade dos Açores.

Objectives for management of socio-ecological systems in the Great Barrier Reef region, Australia

A wide range of goals and objectives have to be taken into account in natural resources management. Defining these objectives in operational terms, including dimensions such as sustainability, productivity, and equity, is by no means easy, especially if they must capture the diversity of community and stakeholder values. This is especially true in the coastal zone where land activities affect regional marine ecosystems. In this study, the aim was firstly to identify and hierarchically organise the goals and objectives for coastal systems, as defined by local stakeholders. Two case study areas are used within the Great Barrier Reef region being Mackay and Bowen–Burdekin. Secondly, the aim was to identify similarities between the case study results and thus develop a generic set of goals to be used as a starting point in other coastal communities. Results show that overarching high-level goals have nested sub-goals that contain a set of more detailed regional objectives. The similarities in high-level environmental, governance, and socio-economic goals suggest that regionally specific objectives can be developed based on a generic set of goals. The prominence of governance objectives reflects local stakeholder perceptions that current coastal zone management is not achieving the outcomes they feel important and that there is a need for increased community engagement and co-management. More importantly, it raises the question of how to make issues relevant for the local community and entice participation in the local management of public resources to achieve sustainable environmental, social, and economic management outcomes. © 2015 Springer-Verlag Berlin Heidelberg

Assessing and reducing vulnerability to climate change: Moving from theory to practical decision-support

As climate change continues to impact socio-ecological systems, tools that assist conservation managers to understand vulnerability and target adaptations are essential. Quantitative assessments of vulnerability are rare because available frameworks are complex and lack guidance for dealing with data limitations and integrating across scales and disciplines. This paper describes a semi-quantitative method for assessing vulnerability to climate change that integrates socio-ecological factors to address management objectives and support decision-making. The method applies a framework first adopted by the Intergovernmental Panel on Climate Change and uses a structured 10-step process. The scores for each framework element are normalized and multiplied to produce a vulnerability score and then the assessed components are ranked from high to low vulnerability. Sensitivity analyses determine which indicators most influence the analysis and the resultant decision-making process so data quality for these indicators can be reviewed to increase robustness. Prioritisation of components for conservation considers other economic, social and cultural values with vulnerability rankings to target actions that reduce vulnerability to climate change by decreasing exposure or sensitivity and/or increasing adaptive capacity. This framework provides practical decision-support and has been applied to marine ecosystems and fisheries, with two case applications provided as examples: (1) food security in Pacific Island nations under climate-driven fish declines, and (2) fisheries in the Gulf of Carpentaria, northern Australia. The step-wise process outlined here is broadly applicable and can be undertaken with minimal resources using existing data, thereby having great potential to inform adaptive natural resource management in diverse locations.

Management and development of enterprise

The research work is devoted to actual problems of development management of industrial enterprises. The general purpose of this work is the choice and justification of rational enterprise development evaluation model and subsequent application of it for assessment of enterprise development level and also forming of recommendations for enterprise management. Theoretical aspects of development management of enterprises were generalized. The approaches to understanding the essence of development enterprise category and its types were considered. It was investigated the evaluation models of enterprise development, their advantages and disadvantages and the difficulties of their implementation. The requirements for formation of the evaluation system of the enterprise development were summarized. It was determined the features of the formation and application of an Index of Enterprise Development. In the empirical part, data about investigated enterprises was collected from their official websites and also complemented with further data from other statistical websites. The analysis was based on the annual financial statements of companies. To assess the level of enterprise development were chosen model proposed by Feshchur and Samulyak (2010). This model involves the calculation of the Index of Enterprise Development using partial indicators, their reference values and weight. It was conducted an analysis of the development of Ukrainian enterprises that produce sauces. OJSC “LZHK” had the highest value of Index of Enterprise Development, in 2013 and 2015, that consisted 0,78 and 0,76 respectively. In 2014 the highest value for the Index belonged to PJSC “Volynholdinh” and amounted 0,74. OJSC “LZHK” had the highest average value of Index of Enterprise Development by the result of 2013-2015 years, and it consisted 0,70. PJSC “Chumak” had the lowest average value of Index of Enterprise Development obtained the result 0,59. In order to raise the enterprise development level, it was suggested to reduce production costs and staff turnover, increase the involvement of employees.

Risk management for mathematical optimization under uncertainty

We present a general multistage stochastic mixed 0-1 problem where the uncertainty appears everywhere in the objective function, constraints matrix and right-hand-side. The uncertainty is represented by a scenario tree that can be a symmetric or a nonsymmetric one. The stochastic model is converted in a mixed 0-1 Deterministic Equivalent Model in compact representation. Due to the difficulty of the problem, the solution offered by the stochastic model has been traditionally obtained by optimizing the objective function expected value (i.e., mean) over the scenarios, usually, along a time horizon. This approach (so named risk neutral) has the inconvenience of providing a solution that ignores the variance of the objective value of the scenarios and, so, the occurrence of scenarios with an objective value below the expected one. Alternatively, we present several approaches for risk averse management, namely, a scenario immunization strategy, the optimization of the well known Value-at-Risk (VaR) and several variants of the Conditional Value-at-Risk strategies, the optimization of the expected mean minus the weighted probability of having a "bad" scenario to occur for the given solution provided by the model, the optimization of the objective function expected value subject to stochastic dominance constraints (SDC) for a set of profiles given by the pairs of threshold objective values and either bounds on the probability of not reaching the thresholds or the expected shortfall over them, and the optimization of a mixture of the VaR and SDC strategies.

Parcellation of fMRI datasets with ICA and PLS: a data driven approach

Inter-subject parcellation of functional Magnetic Resonance Imaging (fMRI) data based on a standard General Linear Model (GLM) and spectral clustering was recently proposed as a means to alleviate the issues associated with spatial normalization in fMRI. However, for all its appeal, a GLM-based parcellation approach introduces its own biases, in the form of a priori knowledge about the shape of Hemodynamic Response Function (HRF) and task-related signal changes, or about the subject behaviour during the task. In this paper, we introduce a data-driven version of the spectral clustering parcellation, based on Independent Component Analysis (ICA) and Partial Least Squares (PLS) instead of the GLM. First, a number of independent components are automatically selected. Seed voxels are then obtained from the associated ICA maps and we compute the PLS latent variables between the fMRI signal of the seed voxels (which covers regional variations of the HRF) and the principal components of the signal across all voxels. Finally, we parcellate all subjects data with a spectral clustering of the PLS latent variables. We present results of the application of the proposed method on both single-subject and multi-subject fMRI datasets. Preliminary experimental results, evaluated with intra-parcel variance of GLM t-values and PLS derived t-values, indicate that this data-driven approach offers improvement in terms of parcellation accuracy over GLM based techniques.

Metodología Demand Driven para mejora de servicio y reducción de costos

Actualmente, para la planeación de la cadena de suministro, las organizaciones utilizan métodos convencionales basados en modelos estadísticos que miran el pasado y no reconocen avances -- Con este estudio se busca proyectar el futuro estos procesos -- Para lograrlo se tiene en cuenta la metodología Demand Driven que, para cambiar esta situación, basa su teoría en la adaptación de la cadena logística para reaccionar ante la venta en tiempo real, mediante la organización de buffers o pequeñas cajas de inventario que según las características de la cadena tendrán distintas propiedades para garantizar siempre la disponibilidad de stock, al menor coste y cantidad posible -- Se pretende demostrar la metodología mediante un caso de empresa: la viabilidad del sistema para garantizar la reducción del capital invertido en inventarios, garantizando mayor flujo de capital para inversión en nuevos aspectos; se presupone mejora en servicio que se traduce en mayores ventas para la compañía y reducción de costos al tener menor nivel de inventarios -- Al realizar el ejemplo empresarial, este estudio entrega a los líderes de las organizaciones las herramientas necesarias para toma de decisiones sobre cambios estructurales en la forma de realizar su proceso de planeación y ventas operacionales en busca de adaptaciones a las necesidades del mercado cambiante y exigente en el que vivimos hoy, donde los consumidores buscan bajo costo, alta calidad y disponibilidad a la mano -- En este estudio se puede observar cómo se pueden incrementar los ingresos en un 20% con sólo mejorar el nivel de servicio y entregas a tiempo a los clientes

A demand-driven analysis of tourist accommodation price: A quantile regression of room bookings

Tourist accommodation expenditure is a widely investigated topic as it represents a major contribution to the total tourist expenditure. The identification of the determinant factors is commonly based on supply-driven applications while little research has been made on important travel characteristics. This paper proposes a demand-driven analysis of tourist accommodation price by focusing on data generated from room bookings. The investigation focuses on modeling the relationship between key travel characteristics and the price paid to book the accommodation. To accommodate the distributional characteristics of the expenditure variable, the analysis is based on the estimation of a quantile regression model. The findings support the econometric approach used and enable the elaboration of relevant managerial implications.

Changes in the storage and sink of carbon dioxide in subsurface atmospheres controlled by climate-driven processes: the case of the Ojo Guareña karst system

A comprehensive environmental monitoring program was conducted in the Ojo Guareña cave system (Spain), one of the longest cave systems in Europe, to assess the magnitude of the spatiotemporal changes in carbon dioxide gas (CO2) in the cave–soil–atmosphere profile. The key climate-driven processes involved in gas exchange, primarily gas diffusion and cave ventilation due to advective forces, were characterized. The spatial distributions of both processes were described through measurements of CO2 and its carbon isotopic signal (δ13C[CO2]) from exterior, soil and cave air samples analyzed by cavity ring-down spectroscopy (CRDS). The trigger mechanisms of air advection (temperature or air density differences or barometric imbalances) were controlled by continuous logging systems. Radon monitoring was also used to characterize the changing airflow that results in a predictable seasonal or daily pattern of CO2 concentrations and its carbon isotopic signal. Large daily oscillations of CO2 levels, ranging from 680 to 1900 ppm day−1 on average, were registered during the daily oscillations of the exterior air temperature around the cave air temperature. These daily variations in CO2 concentration were unobservable once the outside air temperature was continuously below the cave temperature and a prevailing advective-renewal of cave air was established, such that the daily-averaged concentrations of CO2 reached minimum values close to atmospheric background. The daily pulses of CO2 and other tracer gases such as radon (222Rn) were smoothed in the inner cave locations, where fluctuation of both gases was primarily correlated with medium-term changes in air pressure. A pooled analysis of these data provided evidence that atmospheric air that is inhaled into dynamically ventilated caves can then return to the lower troposphere as CO2-rich cave air.

Design of composites for thermal management: Aluminum reinforced with diamond-containing bimodal particle mixtures

A new design route is proposed in order to fabricate aluminum matrix diamond-containing composite materials with optimized values of thermal conductivity (TC) for thermal management applications. The proper size ratio and proportions of particulate diamond–diamond and diamond–SiC bimodal mixtures are selected based on calculations with predictive schemes, which combine two main issues: (i) the volume fraction of the packed particulate mixtures, and (ii) the influence of different types of particulates (with intrinsically different metal/reinforcement interfacial thermal conductances) on the overall thermal conductivity of the composite material. The calculated results are validated by comparison with measurements on composites fabricated by gas pressure infiltration of aluminum into preforms of selected compositions of particle mixtures. Despite the relatively low quality (low price) of the diamond particles used in this work, outstanding values of TC are encountered: a maximum of 770 W/m K for Al/diamond–diamond and values up to 690 W/m K for Al/diamond–SiC.

Is financial instability male-driven? Gender and cognitive skills in experimental asset markets

The hypothesis that price stability would reliably increase with the fraction of women operating in financial markets has been frequently suggested in policy discussions. To test this hypothesis we conducted 10 male-only, 10 female-only and 10 mixed-gender experimental asset markets, and compared the effects of gender composition, confidence, risk attitude and cognitive skills. Male and female markets have comparable volatility and deviations from fundamentals, whereas mixed-gender markets are substantially more stable. On the other hand, higher average cognitive skills of the group are associated with reduced market volatility. Individual-level analysis shows that subjects with higher cognitive skills trade at prices closer to fundamental values and earn significantly higher profits; similarly, mixed markets exhibit lower mispricing, particularly for traders with lower cognitive skills. Our results are demonstrated to hold in other experimental asset market studies, suggesting that a mixed-gender composition reduces mispricing across different types of asset markets.

Influence of management on plankton community of fishponds during the dry and rainy seasons

Aim: The present investigation aims at evaluating attention to the occurrence and abundance of planktonic communities in fishponds and their relationships to the management employed; Methods: Seven fishponds (V1-V7) fertilized with different treatments were analyzed by monthly sampling, taken between July and December/07, during both dry and rainy seasons; Results: Euglenophyceae and Chlorophyceae were most representative during the studied period. In the fishpond with organic fertilizer Cyanobacteria was more than 65 and 90% of total phytoplankton abundance in September and August, mainly represented by Microcystis sp. (14,595 and 22,500 ind.L-1, respectively). An inverse relationship occurred between Copepoda and Cladocera, and Copepoda and Rotifera were present in all fishponds during the both seasons. Diversity (H') and species richness was low and equitability indices generally showed the highest values for zooplankton. The lowest values were observed for phytoplankton during the rainy season; Conclusions: The use of organic fertilizer and the random emptying of the fishponds affected directly and species diversity and richness, with dominance of Cyanobacteria emphasizing the need to adopt a management technique to increase fishponds productivity and consequently, fish production.

AN ASSESSMENT OF FLOW REGIME MAPS AND A NUMERICAL HEAT TRANSFER CORRELATION FOR THE STRATIFIED/ANNULAR REGIME OF SHEAR-DRIVEN INTERNAL CONDENSING FLOWS

Several modern-day cooling applications require the incorporation of mini/micro-channel shear-driven flow condensers. There are several design challenges that need to be overcome in order to meet those requirements. The difficulty in developing effective design tools for shear-driven flow condensers is exacerbated due to the lack of a bridge between the physics-based modelling of condensing flows and the current, popular approach based on semi-empirical heat transfer correlations. One of the primary contributors of this disconnect is a lack of understanding caused by the fact that typical heat transfer correlations eliminate the dependence of the heat transfer coefficient on the method of cooling employed on the condenser surface when it may very well not be the case. This is in direct contrast to direct physics-based modeling approaches where the thermal boundary conditions have a direct and huge impact on the heat transfer coefficient values. Typical heat transfer correlations instead introduce vapor quality as one of the variables on which the value of the heat transfer coefficient depends. This study shows how, under certain conditions, a heat transfer correlation from direct physics-based modeling can be equivalent to typical engineering heat transfer correlations without making the same apriori assumptions. Another huge factor that raises doubts on the validity of the heat-transfer correlations is the opacity associated with the application of flow regime maps for internal condensing flows. It is well known that flow regimes influence heat transfer rates strongly. However, several heat transfer correlations ignore flow regimes entirely and present a single heat transfer correlation for all flow regimes. This is believed to be inaccurate since one would expect significant differences in the heat transfer correlations for different flow regimes. Several other studies present a heat transfer correlation for a particular flow regime - however, they ignore the method by which extents of the flow regime is established. This thesis provides a definitive answer (in the context of stratified/annular flows) to: (i) whether a heat transfer correlation can always be independent of the thermal boundary condition and represented as a function of vapor quality, and (ii) whether a heat transfer correlation can be independently obtained for a flow regime without knowing the flow regime boundary (even if the flow regime boundary is represented through a separate and independent correlation). To obtain the results required to arrive at an answer to these questions, this study uses two numerical simulation tools - the approximate but highly efficient Quasi-1D simulation tool and the exact but more expensive 2D Steady Simulation tool. Using these tools and the approximate values of flow regime transitions, a deeper understanding of the current state of knowledge in flow regime maps and heat transfer correlations in shear-driven internal condensing flows is obtained. The ideas presented here can be extended for other flow regimes of shear-driven flows as well. Analogous correlations can also be obtained for internal condensers in the gravity-driven and mixed-driven configuration.

DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.

Business Process Management and Process Mining within a Real Business Environment: An Empirical Analysis of Event Logs Data in a Consulting Project

Il presente elaborato esplora l’attitudine delle organizzazioni nei confronti dei processi di business che le sostengono: dalla semi-assenza di struttura, all’organizzazione funzionale, fino all’avvento del Business Process Reengineering e del Business Process Management, nato come superamento dei limiti e delle problematiche del modello precedente. All’interno del ciclo di vita del BPM, trova spazio la metodologia del process mining, che permette un livello di analisi dei processi a partire dagli event data log, ossia dai dati di registrazione degli eventi, che fanno riferimento a tutte quelle attività supportate da un sistema informativo aziendale. Il process mining può essere visto come naturale ponte che collega le discipline del management basate sui processi (ma non data-driven) e i nuovi sviluppi della business intelligence, capaci di gestire e manipolare l’enorme mole di dati a disposizione delle aziende (ma che non sono process-driven). Nella tesi, i requisiti e le tecnologie che abilitano l’utilizzo della disciplina sono descritti, cosi come le tre tecniche che questa abilita: process discovery, conformance checking e process enhancement. Il process mining è stato utilizzato come strumento principale in un progetto di consulenza da HSPI S.p.A. per conto di un importante cliente italiano, fornitore di piattaforme e di soluzioni IT. Il progetto a cui ho preso parte, descritto all’interno dell’elaborato, ha come scopo quello di sostenere l’organizzazione nel suo piano di improvement delle prestazioni interne e ha permesso di verificare l’applicabilità e i limiti delle tecniche di process mining. Infine, nell’appendice finale, è presente un paper da me realizzato, che raccoglie tutte le applicazioni della disciplina in un contesto di business reale, traendo dati e informazioni da working papers, casi aziendali e da canali diretti. Per la sua validità e completezza, questo documento è stata pubblicato nel sito dell'IEEE Task Force on Process Mining.