Analysing factors determining the adoption of environmental management measures on the highlands of Ethiopia

A study was conducted on the highlands of Ethiopia to identify and analyse the factors determining the adoption of environmental management measures. In 1985, Ethiopia was classified into low –and high-potential areas based on the suitability of the natural environment for rain-fed agriculture. To address these objectives, case study areas were selected from low-potential and high-potential areas randomly. Data were collected through face-to-face interview and key informants, focus group discussion and field observation. In the low-potential areas, the physical environment ‒ particularly soil and forest environments have shown substantial recovery. Similarly, the water environment has improved. However, in the high-potential areas sampled, these resources are still being degraded. Clear understanding of the benefits of soil conservation structures by farmers, active involvement and technical support from the government and full and genuine participation of farmers in communal environmental resources management activities were found to be main factors in the adoption of environmental management measures.

SMARTDIAB: a communication and information technology approach for the intelligent monitoring, management and follow-up of type 1 diabetes patients

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.

A communication and information technology infrastructure for real time monitoring and management of type 1 diabetes patients

This paper is focused on the integration of state-of-the-art technologies in the fields of telecommunications, simulation algorithms, and data mining in order to develop a Type 1 diabetes patient's semi to fully-automated monitoring and management system. The main components of the system are a glucose measurement device, an insulin delivery system (insulin injection or insulin pumps), a mobile phone for the GPRS network, and a PDA or laptop for the Internet. In the medical environment, appropriate infrastructure for storage, analysis and visualizing of patients' data has been implemented to facilitate treatment design by health care experts.

Taking action against ocean acidification: A review of management and policy options

Ocean acidification has emerged over the last two decades as one of the largest threats to marine organisms and ecosystems. However, most research efforts on ocean acidification have so far neglected management and related policy issues to focus instead on understanding its ecological and biogeochemical implications. This shortfall is addressed here with a systematic, international and critical review of management and policy options. In particular, we investigate the assumption that fighting acidification is mainly, but not only, about reducing CO2 emissions, and explore the leeway that this emerging problem may open in old environmental issues. We review nine types of management responses, initially grouped under four categories: preventing ocean acidification; strengthening ecosystem resilience; adapting human activities; and repairing damages. Connecting and comparing options leads to classifying them, in a qualitative way, according to their potential and feasibility. While reducing CO2 emissions is confirmed as the key action that must be taken against acidification, some of the other options appear to have the potential to buy time, e.g. by relieving the pressure of other stressors, and help marine life face unavoidable acidification. Although the existing legal basis to take action shows few gaps, policy challenges are significant: tackling them will mean succeeding in various areas of environmental management where we failed to a large extent so far.

Sustainable Land Management (SLM) Practices in Drylands: How Do They Address Desertification Threats?

Managing land sustainably is a huge challenge, especially under harsh climatic conditions such as those found in drylands. The socio-economic situation can also pose challenges, as dryland regions are often characterized by remoteness, marginality, low-productive farming, weak institutions, and even conflict. With threats from climate change, disputes over water, competing claims on land, and migration increasing worldwide, the demands for sustainable land management (SLM) measures will only increase in the future. Within the EU-funded DESIRE project, researchers and stakeholders jointly identified existing SLM technologies and approaches in 17 dryland study sites located in the Mediterranean and around the world. In order to evaluate and share this valuable SLM experience, local researchers documented the SLM technologies and approaches in collaboration with land users, utilizing the internationally recognized WOCAT questionnaires. This article provides an analysis of 30 technologies and 8 approaches, enabling an initial evaluation of how SLM addresses prevalent dryland threats, such as water scarcity, soil degradation, vegetation degradation and low production, climate change, resource use conflicts, and migration. Among the impacts attributed to the documented technologies, those mentioned most were diversified and enhanced production and better management of water and soil degradation, whether through water harvesting, improving soil moisture, or reducing runoff. Favorable local-scale cost–benefit relationships were mainly found when considered over the long term. Nevertheless, SLM was found to improve people’s livelihoods and prevent further outmigration. More field research is needed to reinforce expert assessments of SLM impacts and provide the necessary evidence-based rationale for investing in SLM.

Service Level Agreements-Driven Management of Distributed Applications in Cloud Computing Environments

Cloud Computing enables provisioning and distribution of highly scalable services in a reliable, on-demand and sustainable manner. However, objectives of managing enterprise distributed applications in cloud environments under Service Level Agreement (SLA) constraints lead to challenges for maintaining optimal resource control. Furthermore, conflicting objectives in management of cloud infrastructure and distributed applications might lead to violations of SLAs and inefficient use of hardware and software resources. This dissertation focusses on how SLAs can be used as an input to the cloud management system, increasing the efficiency of allocating resources, as well as that of infrastructure scaling. First, we present an extended SLA semantic model for modelling complex service-dependencies in distributed applications, and for enabling automated cloud infrastructure management operations. Second, we describe a multi-objective VM allocation algorithm for optimised resource allocation in infrastructure clouds. Third, we describe a method of discovering relations between the performance indicators of services belonging to distributed applications and then using these relations for building scaling rules that a CMS can use for automated management of VMs. Fourth, we introduce two novel VM-scaling algorithms, which optimally scale systems composed of VMs, based on given SLA performance constraints. All presented research works were implemented and tested using enterprise distributed applications.

Service level agreements-driven management of distributed applications in cloud computing environments

Advancements in cloud computing have enabled the proliferation of distributed applications, which require management and control of multiple services. However, without an efficient mechanism for scaling services in response to changing workload conditions, such as number of connected users, application performance might suffer, leading to violations of Service Level Agreements (SLA) and possible inefficient use of hardware resources. Combining dynamic application requirements with the increased use of virtualised computing resources creates a challenging resource Management context for application and cloud-infrastructure owners. In such complex environments, business entities use SLAs as a means for specifying quantitative and qualitative requirements of services. There are several challenges in running distributed enterprise applications in cloud environments, ranging from the instantiation of service VMs in the correct order using an adequate quantity of computing resources, to adapting the number of running services in response to varying external loads, such as number of users. The application owner is interested in finding the optimum amount of computing and network resources to use for ensuring that the performance requirements of all her/his applications are met. She/he is also interested in appropriately scaling the distributed services so that application performance guarantees are maintained even under dynamic workload conditions. Similarly, the infrastructure Providers are interested in optimally provisioning the virtual resources onto the available physical infrastructure so that her/his operational costs are minimized, while maximizing the performance of tenants’ applications. Motivated by the complexities associated with the management and scaling of distributed applications, while satisfying multiple objectives (related to both consumers and providers of cloud resources), this thesis proposes a cloud resource management platform able to dynamically provision and coordinate the various lifecycle actions on both virtual and physical cloud resources using semantically enriched SLAs. The system focuses on dynamic sizing (scaling) of virtual infrastructures composed of virtual machines (VM) bounded application services. We describe several algorithms for adapting the number of VMs allocated to the distributed application in response to changing workload conditions, based on SLA-defined performance guarantees. We also present a framework for dynamic composition of scaling rules for distributed service, which used benchmark-generated application Monitoring traces. We show how these scaling rules can be combined and included into semantic SLAs for controlling allocation of services. We also provide a detailed description of the multi-objective infrastructure resource allocation problem and various approaches to satisfying this problem. We present a resource management system based on a genetic algorithm, which performs allocation of virtual resources, while considering the optimization of multiple criteria. We prove that our approach significantly outperforms reactive VM-scaling algorithms as well as heuristic-based VM-allocation approaches.