844 resultados para environment management


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Smart Environments are currently considered a key factor to connect the physical world with the information world. A Smart Environment can be defined as the combination of a physical environment, an infrastructure for data management (called Smart Space), a collection of embedded systems gathering heterogeneous data from the environment and a connectivity solution to convey these data to the Smart Space. With this vision, any application which takes advantages from the environment could be devised, without the need to directly access to it, since all information are stored in the Smart Space in a interoperable format. Moreover, according to this vision, for each entity populating the physical environment, i.e. users, objects, devices, environments, the following questions can be arise: “Who?”, i.e. which are the entities that should be identified? “Where?” i.e. where are such entities located in physical space? and “What?” i.e. which attributes and properties of the entities should be stored in the Smart Space in machine understandable format, in the sense that its meaning has to be explicitly defined and all the data should be linked together in order to be automatically retrieved by interoperable applications. Starting from this the location detection is a necessary step in the creation of Smart Environments. If the addressed entity is a user and the environment a generic environment, a meaningful way to assign the position, is through a Pedestrian Tracking System. In this work two solution for these type of system are proposed and compared. One of the two solution has been studied and developed in all its aspects during the doctoral period. The work also investigates the problem to create and manage the Smart Environment. The proposed solution is to create, by means of natural interactions, links between objects and between objects and their environment, through the use of specific devices, i.e. Smart Objects

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Biomedical analyses are becoming increasingly complex, with respect to both the type of the data to be produced and the procedures to be executed. This trend is expected to continue in the future. The development of information and protocol management systems that can sustain this challenge is therefore becoming an essential enabling factor for all actors in the field. The use of custom-built solutions that require the biology domain expert to acquire or procure software engineering expertise in the development of the laboratory infrastructure is not fully satisfactory because it incurs undesirable mutual knowledge dependencies between the two camps. We propose instead an infrastructure concept that enables the domain experts to express laboratory protocols using proper domain knowledge, free from the incidence and mediation of the software implementation artefacts. In the system that we propose this is made possible by basing the modelling language on an authoritative domain specific ontology and then using modern model-driven architecture technology to transform the user models in software artefacts ready for execution in a multi-agent based execution platform specialized for biomedical laboratories.

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This thesis presents SEELF (Sustainable EEL fishery) Index, a methodology for evaluation of European eel (Anguilla anguilla) for the implementation of an effective Eel Management Plan, as defined by EU Regulation No.1100/2007. SEELF uses internal and external indices, age and blood parameters, and selects suitable specimen for restocking; it is also a reliable tool for eel stock management. In fact, SEELF Index, was developed in two versions: SEELF A, to be used in field operations (catch&release, eel status monitoring) and SEELF B to be used for quality control (food production) and research (eel status monitoring). Health status was evaluated also by biomarker analysis (ChE), and data were compared with age of eel. Age determination was performed with otolith reading and fish scale reading and a calibration between the two methods was possible. The study area was the Comacchio lagoon, a brackish coastal lagoon in Italy, well known as an example of suitable environment for eel fishery, where the capability to use the local natural resources has long been a key factor for a successful fishery management. Comacchio lagoon is proposed as an area where an effective EMP can be performed, in agreement with the main features (management of basins, reduction of mortality due to predators,etc.) highlighted for designation of European Restocking Area (ERA). The ERA is a new concept, proposed as a pillar of a new strategy on eel management and conservation. Furthermore, the features of ERAs can be useful in the framework of European Scale Eel Management Plan (ESEMP), proposed as a European scale implementation of EMP, providing a more effectiveness of conservation measures for eel management.

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This thesis presents several data processing and compression techniques capable of addressing the strict requirements of wireless sensor networks. After introducing a general overview of sensor networks, the energy problem is introduced, dividing the different energy reduction approaches according to the different subsystem they try to optimize. To manage the complexity brought by these techniques, a quick overview of the most common middlewares for WSNs is given, describing in detail SPINE2, a framework for data processing in the node environment. The focus is then shifted on the in-network aggregation techniques, used to reduce data sent by the network nodes trying to prolong the network lifetime as long as possible. Among the several techniques, the most promising approach is the Compressive Sensing (CS). To investigate this technique, a practical implementation of the algorithm is compared against a simpler aggregation scheme, deriving a mixed algorithm able to successfully reduce the power consumption. The analysis moves from compression implemented on single nodes to CS for signal ensembles, trying to exploit the correlations among sensors and nodes to improve compression and reconstruction quality. The two main techniques for signal ensembles, Distributed CS (DCS) and Kronecker CS (KCS), are introduced and compared against a common set of data gathered by real deployments. The best trade-off between reconstruction quality and power consumption is then investigated. The usage of CS is also addressed when the signal of interest is sampled at a Sub-Nyquist rate, evaluating the reconstruction performance. Finally the group sparsity CS (GS-CS) is compared to another well-known technique for reconstruction of signals from an highly sub-sampled version. These two frameworks are compared again against a real data-set and an insightful analysis of the trade-off between reconstruction quality and lifetime is given.

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Over the last decades the impact of natural disasters to the global environment is becoming more and more severe. The number of disasters has dramatically increased, as well as the cost to the global economy and the number of people affected. Among the natural disaster, flood catastrophes are considered to be the most costly, devastating, broad extent and frequent, because of the tremendous fatalities, injuries, property damage, economic and social disruption they cause to the humankind. In the last thirty years, the World has suffered from severe flooding and the huge impact of floods has caused hundreds of thousands of deaths, destruction of infrastructures, disruption of economic activity and the loss of property for worth billions of dollars. In this context, satellite remote sensing, along with Geographic Information Systems (GIS), has become a key tool in flood risk management analysis. Remote sensing for supporting various aspects of flood risk management was investigated in the present thesis. In particular, the research focused on the use of satellite images for flood mapping and monitoring, damage assessment and risk assessment. The contribution of satellite remote sensing for the delineation of flood prone zones, the identification of damaged areas and the development of hazard maps was explored referring to selected cases of study.

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SMARTDIAB is a platform designed to support the monitoring, management, and treatment of patients with type 1 diabetes mellitus (T1DM), by combining state-of-the-art approaches in the fields of database (DB) technologies, communications, simulation algorithms, and data mining. SMARTDIAB consists mainly of two units: 1) the patient unit (PU); and 2) the patient management unit (PMU), which communicate with each other for data exchange. The PMU can be accessed by the PU through the internet using devices, such as PCs/laptops with direct internet access or mobile phones via a Wi-Fi/General Packet Radio Service access network. The PU consists of an insulin pump for subcutaneous insulin infusion to the patient and a continuous glucose measurement system. The aforementioned devices running a user-friendly application gather patient's related information and transmit it to the PMU. The PMU consists of a diabetes data management system (DDMS), a decision support system (DSS) that provides risk assessment for long-term diabetes complications, and an insulin infusion advisory system (IIAS), which reside on a Web server. The DDMS can be accessed from both medical personnel and patients, with appropriate security access rights and front-end interfaces. The DDMS, apart from being used for data storage/retrieval, provides also advanced tools for the intelligent processing of the patient's data, supporting the physician in decision making, regarding the patient's treatment. The IIAS is used to close the loop between the insulin pump and the continuous glucose monitoring system, by providing the pump with the appropriate insulin infusion rate in order to keep the patient's glucose levels within predefined limits. The pilot version of the SMARTDIAB has already been implemented, while the platform's evaluation in clinical environment is being in progress.

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The White Paper is a review of leading scientific knowledge on the role of knowledge management, institutions and economics in monitoring and assessment of land degradation and desertification. It provides key recommendations for more effective policies and actions for combating desertification both withn the UNCCD and beyond. This White Paper is the result of an international collaboration and consultation led jointly by the Association of DesertNet International and the United Nations University - Institute for Water, Environment and Health (UNU-INWEH), of the Dryland Science for Development Consortium (DSD). The findings were presented at the First UNCCD Scientific Conference held during the COP-9 in Buenos Aires, 2009.

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Although sustainable land management (SLM) is widely promoted to prevent and mitigate land degradation and desertification, its monitoring and assessment (M&A) has received much less attention. This paper compiles methodological approaches which to date have been little reported in the literature. It draws lessons from these experiences and identifies common elements and future pathways as a basis for a global approach. The paper starts with local level methods where the World Overview of Conservation Approaches and Technologies (WOCAT) framework catalogues SLM case studies. This tool has been included in the local level assessment of Land Degradation Assessment in Drylands (LADA) and in the EU-DESIRE project. Complementary site-based approaches can enhance an ecological process-based understanding of SLM variation. At national and sub-national levels, a joint WOCAT/LADA/DESIRE spatial assessment based on land use systems identifies the status and trends of degradation and SLM, including causes, drivers and impacts on ecosystem services. Expert consultation is combined with scientific evidence and enhanced where necessary with secondary data and indicator databases. At the global level, the Global Environment Facility (GEF) knowledge from the land (KM:Land) initiative uses indicators to demonstrate impacts of SLM investments. Key lessons learnt include the need for a multi-scale approach, making use of common indicators and a variety of information sources, including scientific data and local knowledge through participatory methods. Methodological consistencies allow cross-scale analyses, and findings are analysed and documented for use by decision-makers at various levels. Effective M&A of SLM [e.g. for United Nations Convention to Combat Desertification (UNCCD)] requires a comprehensive methodological framework agreed by the major players.

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Like other mountain areas in the world, the Hindu Kush-Himalayan (HKH) region is particularly vulnerable to climate change. Ongoing climate change processes are projected to have a high impact on the HKH region, and accelerated warming has been reported in the Himalayas. These climate change impacts will be superimposed on a variety of other environmental and social stresses, adding to the complexity of the issues. The sustainable use of natural resources is crucial to the long-term stability of the fragile mountain ecosystems in the HKH and to sustain the socio-ecological resilience that forms the basis of sustainable livelihoods in the region. In order to be prepared for these challenges, it is important to take stock of previous research. The ‘People and Resource Dynamics Project’ (PARDYP), implemented by International Centre for Integrated Mountain Development (ICIMOD), provides a variety of participatory options for sustainable land management in the HKH region. The PARDYD project was a research for development project that operated in five middle mountain watersheds across the HKH – two in Nepal and one each in China, India, and Pakistan. The project ran from 1996 to 2006 and focused on addressing the marginalisation of mountain farmers, the use and availability of water, issues relating to land and forest degradation and declining soil fertility, the speed of regeneration of degraded land, and the ability of the natural environment to support the growing needs of the region’s increasing population. A key learning from the project was that the opinion of land users is crucial to the acceptance (and, therefore, successful application) of new technologies and approaches. A major challenge at the end of every project is to promote knowledge sharing and encourage the cross-fertilization of ideas (e.g., in the case of PARDYP, with other middle mountain inhabitants and practitioners in the region) and to share lessons learned with a wider audience. This paper will highlight how the PARDYP findings, including ways of addressing soil fertility and water scarcity, have been mainstreamed in the HKH region through capacity building (international, regional, and national training courses), networking, and the provision of backstopping services. In addition, in view of the challenges in watershed management in the HKH connected to environmental change, the lessons learned from the PARDYP are now being used by ICMOD to define and package climate change proof technology options to address climate change adaptation.

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This article introduces a new construct to the field of management called Psychological Sense of Community (PSOC). This is important because management scholars are calling for the creation of communities in organizations in an environment that lacks appropriate construct development. The aims of this article are threefold: (a) develop a working definition of PSOC via a review of the extant literature on PSOC from other disciplines with the goal of translating it into the domain of management, (b) synthesize findings from parallel literatures on the outcomes of PSOC with an eye toward exploring the relevance of such outcomes in management contexts, and (c) assess the value of PSOC as it relates to its uniqueness in relation to other prominent management constructs and its scope of applicability in a variety of management inquiry areas.

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