Impact of sensor nodes scaling and velocity on handover mechanisms for healthcare wireless sensor networks with mobility support

Health promotion in hospital environments can be improved using the most recent information and communication technologies. The Internet connectivity to small sensor nodes carried by patients allows remote access to their bio-signals. To promote these features the healthcare wireless sensor networks (HWSN) are used. In these networks mobility support is a key issue in order to keep patients under realtime monitoring even when they move around. To keep sensors connected to the network, they should change their access points of attachment when patients move to a new coverage area along an infirmary. This process, called handover, is responsible for continuous network connectivity to the sensors. This paper presents a detailed performance evaluation study considering three handover mechanisms for healthcare scenarios (Hand4MAC, RSSI-based, and Backbone-based). The study was performed by simulation using several scenarios with different number of sensors and different moving velocities of sensor nodes. The results show that Hand4MAC is the best solution to guarantee almost continuous connectivity to sensor nodes with less energy consumption.

PIASA: A power and interference aware resource management strategy for heterogeneous workloads in cloud data centers

Cloud data centers have been progressively adopted in different scenarios, as reflected in the execution of heterogeneous applications with diverse workloads and diverse quality of service (QoS) requirements. Virtual machine (VM) technology eases resource management in physical servers and helps cloud providers achieve goals such as optimization of energy consumption. However, the performance of an application running inside a VM is not guaranteed due to the interference among co-hosted workloads sharing the same physical resources. Moreover, the different types of co-hosted applications with diverse QoS requirements as well as the dynamic behavior of the cloud makes efficient provisioning of resources even more difficult and a challenging problem in cloud data centers. In this paper, we address the problem of resource allocation within a data center that runs different types of application workloads, particularly CPU- and network-intensive applications. To address these challenges, we propose an interference- and power-aware management mechanism that combines a performance deviation estimator and a scheduling algorithm to guide the resource allocation in virtualized environments. We conduct simulations by injecting synthetic workloads whose characteristics follow the last version of the Google Cloud tracelogs. The results indicate that our performance-enforcing strategy is able to fulfill contracted SLAs of real-world environments while reducing energy costs by as much as 21%.

Federated IaaS Resource Brokerage

This paper presents the CloudAnchor brokerage platform for the transaction of single provider as well as federated Infrastructure as a Service (IaaS) resources. The platform, which is a layered Multi-Agent System (MAS), provides multiple services, including (consumer or provider) business registration and deregistration, provider coalition creation and termination, provider lookup and invitation and negotiation services regarding brokerage, coalitions and resources. Providers, consumers and virtual providers, representing provider coalitions, are modelled by dedicated agents within the platform. The main goal of the platform is to negotiate and establish Service Level Agreements (SLA). In particular, the platform contemplates the establishment of brokerage SLA – bSLA – between the platform and each provider or consumer, coalition SLA – cSLA – between the members of a coalition of providers and resource SLA – rSLA – between a consumer and a provider. Federated resources are detained and negotiated by virtual providers on behalf of the corresponding coalitions of providers.

Potencial Utilização de um Enterprise Resource Planning System para Suportar Práticas de Gestão de Conhecimento nas Organizações. Uma Aplicação com Dynamics Nav da Microsoft

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação

A technology diffusion perspective of Enterprise Resource Planning across European Small and Medium Enterprises: from determinants to Use to Value

Tese apresentada como requisito parcial para obtenção do grau de Doutor em Gestão de Informação

A conceptual model to approach the scaling process of social entreprises: The case of Cozinha com Alma

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

A new methodology for urban wind resource assessment

In the latest years the wind energy sector experienced an exponential growth all over the world. What started as a deployment of onshore projects, soon moved to offshore and, more recently to the urban environment within the context of smart cities and renewable micro-generation. However, urban wind projects using micro turbines do not have enough profit margins to enable the setup of comprehensive and expensive measurement campaigns, a standard procedure for the deployment of large wind parks. To respond to the wind assessment needs of the future smart cities a new and simple methodology for urban wind resource assessment was developed. This methodology is based on the construction of a surface involving a built area in order to estimate the wind potential by treating it as very complex orography. This is a straightforward methodology that allows estimating the sustainable urban wind potential, being suitable to map the urban wind resource in large areas. The methodology was applied to a case study and the results enabled the wind potential assessment of a large urban area being consistent with experimental data obtained in the case study area, with maximum deviations of the order of 10% (mean wind speed) and 20% (power density).

Analysis of world economic variables using multidimensional scaling

Waves of globalization reflect the historical technical progress and modern economic growth. The dynamics of this process are here approached using the multidimensional scaling (MDS) methodology to analyze the evolution of GDP per capita, international trade openness, life expectancy, and education tertiary enrollment in 14 countries. MDS provides the appropriate theoretical concepts and the exact mathematical tools to describe the joint evolution of these indicators of economic growth, globalization, welfare and human development of the world economy from 1977 up to 2012. The polarization dance of countries enlightens the convergence paths, potential warfare and present-day rivalries in the global geopolitical scene.

Scaling In-Memory databases on multicores

Current computer systems have evolved from featuring only a single processing unit and limited RAM, in the order of kilobytes or few megabytes, to include several multicore processors, o↵ering in the order of several tens of concurrent execution contexts, and have main memory in the order of several tens to hundreds of gigabytes. This allows to keep all data of many applications in the main memory, leading to the development of inmemory databases. Compared to disk-backed databases, in-memory databases (IMDBs) are expected to provide better performance by incurring in less I/O overhead. In this dissertation, we present a scalability study of two general purpose IMDBs on multicore systems. The results show that current general purpose IMDBs do not scale on multicores, due to contention among threads running concurrent transactions. In this work, we explore di↵erent direction to overcome the scalability issues of IMDBs in multicores, while enforcing strong isolation semantics. First, we present a solution that requires no modification to either database systems or to the applications, called MacroDB. MacroDB replicates the database among several engines, using a master-slave replication scheme, where update transactions execute on the master, while read-only transactions execute on slaves. This reduces contention, allowing MacroDB to o↵er scalable performance under read-only workloads, while updateintensive workloads su↵er from performance loss, when compared to the standalone engine. Second, we delve into the database engine and identify the concurrency control mechanism used by the storage sub-component as a scalability bottleneck. We then propose a new locking scheme that allows the removal of such mechanisms from the storage sub-component. This modification o↵ers performance improvement under all workloads, when compared to the standalone engine, while scalability is limited to read-only workloads. Next we addressed the scalability limitations for update-intensive workloads, and propose the reduction of locking granularity from the table level to the attribute level. This further improved performance for intensive and moderate update workloads, at a slight cost for read-only workloads. Scalability is limited to intensive-read and read-only workloads. Finally, we investigate the impact applications have on the performance of database systems, by studying how operation order inside transactions influences the database performance. We then propose a Read before Write (RbW) interaction pattern, under which transaction perform all read operations before executing write operations. The RbW pattern allowed TPC-C to achieve scalable performance on our modified engine for all workloads. Additionally, the RbW pattern allowed our modified engine to achieve scalable performance on multicores, almost up to the total number of cores, while enforcing strong isolation.

Proving a market for a startup befor scaling using the hypothesis-driven entrepreneurship process

The year is 2015 and the startup and tech business ecosphere has never seen more activity. In New York City alone, the tech startup industry is on track to amass $8 billion dollars in total funding – the highest in 7 years (CB Insights, 2015). According to the Kauffman Index of Entrepreneurship (2015), this figure represents just 20% of the total funding in the United States. Thanks to platforms that link entrepreneurs with investors, there are simply more funding opportunities than ever, and funding can be initiated in a variety of ways (angel investors, venture capital firms, crowdfunding). And yet, in spite of all this, according to Forbes Magazine (2015), nine of ten startups will fail. Because of the unpredictable nature of the modern tech industry, it is difficult to pinpoint exactly why 90% of startups fail – but the general consensus amongst top tech executives is that “startups make products that no one wants” (Fortune, 2014). In 2011, author Eric Ries wrote a book called The Lean Startup in attempts to solve this all-too-familiar problem. It was in this book where he developed the framework for The Hypothesis-Driven Entrepreneurship Process, an iterative process that aims at proving a market before actually launching a product. Ries discusses concepts such as the Minimum Variable Product, the smallest set of activities necessary to disprove a hypothesis (or business model characteristic). Ries encourages acting briefly and often: if you are to fail, then fail fast. In today’s fast-moving economy, an entrepreneur cannot afford to waste his own time, nor his customer’s time. The purpose of this thesis is to conduct an in-depth of analysis of Hypothesis-Driven Entrepreneurship Process, in order to test market viability of a reallife startup idea, ShowMeAround. This analysis will follow the scientific Lean Startup approach; for the purpose of developing a functional business model and business plan. The objective is to conclude with an investment-ready startup idea, backed by rigorous entrepreneurial study.

Comparison of single and multi-objective evolutionary algorithms for robust link-state routing

Traffic Engineering (TE) approaches are increasingly impor- tant in network management to allow an optimized configuration and resource allocation. In link-state routing, the task of setting appropriate weights to the links is both an important and a challenging optimization task. A number of different approaches has been put forward towards this aim, including the successful use of Evolutionary Algorithms (EAs). In this context, this work addresses the evaluation of three distinct EAs, a single and two multi-objective EAs, in two tasks related to weight setting optimization towards optimal intra-domain routing, knowing the network topology and aggregated traffic demands and seeking to mini- mize network congestion. In both tasks, the optimization considers sce- narios where there is a dynamic alteration in the state of the system, in the first considering changes in the traffic demand matrices and in the latter considering the possibility of link failures. The methods will, thus, need to simultaneously optimize for both conditions, the normal and the altered one, following a preventive TE approach towards robust configurations. Since this can be formulated as a bi-objective function, the use of multi-objective EAs, such as SPEA2 and NSGA-II, came nat- urally, being those compared to a single-objective EA. The results show a remarkable behavior of NSGA-II in all proposed tasks scaling well for harder instances, and thus presenting itself as the most promising option for TE in these scenarios.

Audit quality: a resource-based perspective

This study considers the Certified Public Accountants (CPA) partners of the audit firms as the core producers of these knowledge-based organisations and investigates the effects of audit firm-level of CPA partners’ experience in audit quality. Audit quality was measured through the audit clients’ earnings quality. The main findings suggested that the audit firm-level of CPA partners’ general experience is positively associated with audit quality. Also, the audit firm-level of CPA partners’ experience acquired with prior audit firms significantly improves audit quality. Concerning the effects of audit firm-level of CPA partners’ experience acquired in the current audit firm in audit quality, results were statistically insignificant. Finally, we found that changes in audit firm-level of CPA partners’ general experience also influence audit quality. We verified that the more positive the change in the audit firm stock of CPA partners’ general experience, the higher is audit quality. We interpret this finding as revealing that the year-to-year changes in audit firm’s stock of CPA partners’ general experience is the mechanism allowing these firms to achieve and maintain the critical mass of this resource and reach higher performance levels. We further showed that our main results were not driven by the engagement partner experience.

Omnivory and resource - sharing in nutrient - deficient Rio Negro waters: stabilization of biodiversity?

Amazonian biodiversity is notorious, this is also valid for the fauna of the mineral-deficient waters of the Rio Negro System. Some 25 years of research on the benthic fauna of Central Amazonian streams resulted in species-rich foodwebs with a high degree of omnivory within dense animal communities. To exemplify the taxonomic range of omnivorous consumers, the detailed resource spectra of 18 consumer species, including Protozoa (2 species), Platyhelminthes (1 species), insects (2 species), fish (6 species) and shrimps (Decapoda, 7 species), associated primarily with the benthic habitats of Rio Negro tributaries, are presented. Special features of omnivory are characterized, and the importance of litter-decomposing fungi as essential energy input into the foodwebs is documented. It is shown that general omnivory -diverse omnivore consumers sharing most of the resource types- is a prevalent feature. The relevance of this general omnivory for the maintenance of biodiversity is discussed.

Resource partitioning and ecomorphological variation in two syntopic species of Lebiasinidae (Characiformes) in an Amazonian stream

ABSTRACTResource partitioning is important for species coexistence. Species with similar ecomorphology are potential competitors, especially when phylogenetically close, due to niche conservatism. The aim of this study was to investigate the resource partitioning among populations of two species of lebiasinids (Copella nigrofasciata and Pyrrhulina aff. brevis) that co-occur in a first-order Amazonian stream, analyzing the trophic ecology, feeding strategies and ecomorphological attributes related to the use of food and space by these species. Fish were captured in May and September 2010. The stomach contents of 60 individuals were analyzed and quantified volumetrically to characterize the feeding ecology of both species. Eleven morphological attributes were measured in 20 specimens and combined in nine ecomorphological indices. Both species had an omnivorous-invertivorous diet and consumed predominantly allochthonous items. Both showed a tendency to a generalist diet, but intrapopulational variation in resource use was also detected. Overall feeding niche overlap was high, but differed between seasons: low during the rainy season and high in the dry season. In the latter, the food niche overlap was asymmetric because C. nigrofasciata consumed several prey of P. aff. brevis, which reduced its food spectrum. The ecomorphological analysis suggests that C. nigrofasciatahas greater swimming capacity (greater relative length of caudal peduncle) than P. aff. brevis, which has greater maneuverability and tendency to inhabit lentic environments (greater relative depth of the body). Our results demonstrate that these species have similar trophic ecology and suggest a spatial segregation, given by morphological differences related to locomotion and occupation of habitat, favoring their coexistence.