Crowdsourcing innovation: a proposal for a brokering architecture focused in the innovation needs of SMEs

Crowdsourcing is evolving into powerful outsourcing options for organizations by providing access to the intellectual capital within a vast knowledge community. Innovation brokering services have emerged to facilitate crowdsourcing projects by connecting up companies with potential solution providers within the wider ‘crowd’. Most existing innovation brokering services are primarily aimed at larger organizations, however, Small and Medium Enterprises (SMEs) offer considerable potential for crowdsourcing activity since they are typically the innovation and employment engines in society; they are typically more nimble and responsive to the business environment than the larger companies. SMEs have very different challenges and needs to larger organizations since they have fewer resources, a more limited knowledge and skill base, and immature management practices. Consequently, innovation brokering for SMEs require considerably more support than for larger organizations. This paper identifies the crowdsourcing innovation brokerage facilities needed by SMEs, and presents an architecture that encourages knowledge sharing, development of community, support in mixing and matching capabilities, and management of stakeholders’ risks. Innovation brokering is emerging as a novel business model that is challenging concepts of the traditional value chain and organizational boundaries.

Organization knowledge management change from a complex adaptive systems perpective with ability for ambidexeterity

We are working on the confluence of knowledge management, organizational memory and emergent knowledge with the lens of complex adaptive systems. In order to be fundamentally sustainable organizations search for an adaptive need for managing ambidexterity of day-to-day work and innovation. An organization is an entity of a systemic nature, composed of groups of people who interact to achieve common objectives, making it necessary to capture, store and share interactions knowledge with the organization, this knowledge can be generated in intra-organizational or inter-organizational level. The organizations have organizational memory of knowledge of supported on the Information technology and systems. Each organization, especially in times of uncertainty and radical changes, to meet the demands of the environment, needs timely and sized knowledge on the basis of tacit and explicit. This sizing is a learning process resulting from the interaction that emerges from the relationship between the tacit and explicit knowledge and which we are framing within an approach of Complex Adaptive Systems. The use of complex adaptive systems for building the emerging interdependent relationship, will produce emergent knowledge that will improve the organization unique developing.

Managing contention of software transactional memory in real-time systems

The foreseen evolution of chip architectures to higher number of, heterogeneous, cores, with non-uniform memory and non-coherent caches, brings renewed attention to the use of Software Transactional Memory (STM) as an alternative to lock-based synchronisation. However, STM relies on the possibility of aborting conflicting transactions to maintain data consistency, which impacts on the responsiveness and timing guarantees required by real-time systems. In these systems, contention delays must be (efficiently) limited so that the response times of tasks executing transactions are upperbounded and task sets can be feasibly scheduled. In this paper we defend the role of the transaction contention manager to reduce the number of transaction retries and to help the real-time scheduler assuring schedulability. For such purpose, the contention management policy should be aware of on-line scheduling information.

Non-preemptive and SRP-based fullypreemptive scheduling of real-time Software Transactional Memory

Recent embedded processor architectures containing multiple heterogeneous cores and non-coherent caches renewed attention to the use of Software Transactional Memory (STM) as a building block for developing parallel applications. STM promises to ease concurrent and parallel software development, but relies on the possibility of abort conflicting transactions to maintain data consistency, which in turns affects the execution time of tasks carrying transactions. Because of this fact the timing behaviour of the task set may not be predictable, thus it is crucial to limit the execution time overheads resulting from aborts. In this paper we formalise a FIFO-based algorithm to order the sequence of commits of concurrent transactions. Then, we propose and evaluate two non-preemptive and one SRP-based fully-preemptive scheduling strategies, in order to avoid transaction starvation.

Learning management systems usage on higher education institutions

Learning Management Systems (LMS) are used all over Higher Education Institutions (HEI) and the need to know and understand its adoption and usage arises. However, there is a lack of information about how LMSs are being used, which are the most adopted, whether there is a country adoption standard and which countries use more LMSs. A research team is developing a project that tries to fill this lack of information and provide the needed answers. With this purpose, on a first phase, it a survey was taken place. The results of this survey are presented in this paper. Another purpose of this paper is to disseminate the ongoing project.

Performance management in health centres: evidence from a field study

There has been a growing interest in research on performance measurement and management practices, which seems to reflect researchers’ response to calls for the need to increase the relevance of management accounting research. However, despite the development of the new public management literature, studies involving public sector organizations are relatively small compared to those involving business organizations and extremely limited when it comes to public primary health care organizations. Yet, the economic significance of public health care organizations in the economy of developed countries and the criticisms these organizations regularly face from the public suggests there is a need for research. This is particularly true in the case of research that may lead to improvement in performance measurement and management practices and ultimately to improvements in the way health care organizations use their limited resources in the provision of services to the communities. This study reports on a field study involving three public primary health care organisations. The evidence obtained from interviews and archival data suggests a performance management practices in these institutions lacked consistency and coherence, potentially leading to decreased performance. Hierarchical controls seemed to be very weak and accountability limited, leading to a lack of direction, low motivation and, in some circumstances to insufficient managerial abilities and skills. Also, the performance management systems revealed a number of weaknesses, which suggests that there are various opportunities for improvement in performance in the studied organisations.

Programas de self-management em pacientes com DPOC: uma revisão sistemática

Introdução: Programas de self-management têm como objectivo habilitar os pacientes com estratégias necessárias para levar a cabo procedimentos específicos para a patologia. A última revisão sistemática sobre selfmanagament em DPOC foi realizada em 2007, concluindo-se que ainda não era possível fornecer dados claros e suficientes acerca de recomendações sobre a estrutura e conteúdo de programas de self-managament na DPOC. A presente revisão tem o intuito de complementar a análise da revisão anterior, numa tentativa de inferir a influência do ensino do self-management na DPOC. Objectivos: verificar a influência dos programas de self-management na DPOC, em diversos indicadores relacionados com o estado de saúde do paciente e na sua utilização dos serviços de saúde. Estratégia de busca: pesquisa efectuada nas bases de dados PubMed e Cochrane Collaboration (01/01/2007 – 31/08/2010). Palavras-chave: selfmanagement education, self-management program, COPD e pulmonary rehabilitation. Critérios de Selecção: estudos randomizados sobre programas de selfmanagement na DPOC. Extracção e Análise dos Dados: 2 investigadores realizaram, independentemente, a avaliação e extracção de dados de cada artigo. Resultados: foram considerados 4 estudos randomizados em selfmanagement na DPOC nos quais se verificaram benefícios destes programas em diversas variáveis: qualidade de vida a curto e médio prazo, utilização dos diferentes recursos de saúde, adesões a medicação de rotina, controle das exacerbações e diminuição da sintomatologia. Parece não ocorrer alteração na função pulmonar e no uso de medicação de emergência, sendo inconclusivo o seu efeito na capacidade de realização de exercício. Conclusões: programas de self-management aparentam ter impacto positivo na qualidade de vida, recurso a serviços de saúde, adesão à medicação, planos de acção e níveis de conhecimento da DPOC. Discrepâncias nos critérios de selecção das amostras utilizadas, períodos de seguimento desiguais, consistência das variáveis mensuradas, condicionam a informação disponibilizada sobre este assunto.

Improving management skills through business simulation

A survey was conducted among students of the Accounting and Administration undergraduate degree at ISCAP – IPP (School of Accounting and Administration of Polytechnic Institute of Porto) in order to understand their perception value of their course Business Simulation (BS). This course is provided in a business environment where students can learn by doing through the management of a company as they were in the real life, but risk-free. The learning tasks are provided in an action-oriented format to maximize the learning process. Students learn by doing a set of tasks every session and have also to produce reports and presentations during the course. BS is part of the undergraduate degree of Accounting and Administration at ISCAP – IPP since the beginning of 2003. The questionnaire we used captured the students’ perception about general and specific skills and competencies considered important for managers and accountants in the real life, about the methodology used in the course, which is totally different from the traditional form, and also about the adequacy of the course included as part of the undergraduate degree. The results showed that students’ perception is highly positive and almost all of them think they improve the skills needed for a job during the course. These results are consistent with [1] Adler and Milne’s research in which the authors found that students agree with the use of action-oriented learning tasks in order to provide them the needed attitudes, skills, and knowledge. The improvement of group skills is the most important issue for students, which can be understandable as BS is the only course from the degree in Accounting and Administration they really have to work in groups.

A congestion management and transmission price simulator for competitive electricity markets

In a liberalized electricity market, the Transmission System Operator (TSO) plays a crucial role in power system operation. Among many other tasks, TSO detects congestion situations and allocates the payments of electricity transmission. This paper presents a software tool for congestion management and transmission price determination in electricity markets. The congestion management is based on a reformulated Optimal Power Flow (OPF), whose main goal is to obtain a feasible solution for the re-dispatch minimizing the changes in the dispatch proposed by the market operator. The transmission price computation considers the physical impact caused by the market agents in the transmission network. The final tariff includes existing system costs and also costs due to the initial congestion situation and losses costs. The paper includes a case study for the IEEE 30 bus power system.

Long-term price range forecast applied to risk management using regression models

Long-term contractual decisions are the basis of an efficient risk management. However those types of decisions have to be supported with a robust price forecast methodology. This paper reports a different approach for long-term price forecast which tries to give answers to that need. Making use of regression models, the proposed methodology has as main objective to find the maximum and a minimum Market Clearing Price (MCP) for a specific programming period, and with a desired confidence level α. Due to the problem complexity, the meta-heuristic Particle Swarm Optimization (PSO) was used to find the best regression parameters and the results compared with the obtained by using a Genetic Algorithm (GA). To validate these models, results from realistic data are presented and discussed in detail.

Virtual power players demand response programs management based on locational marginal prices

Power systems have been suffering huge changes mainly due to the substantial increase of distributed generation and to the operation in competitive environments. Virtual power players can aggregate a diversity of players, namely generators and consumers, and a diversity of energy resources, including electricity generation based on several technologies, storage and demand response. Resource management gains an increasing relevance in this competitive context, while demand side active role provides managers with increased demand elasticity. This makes demand response use more interesting and flexible, giving rise to a wide range of new opportunities.This paper proposes a methodology for managing demand response programs in the scope of virtual power players. The proposed method is based on the calculation of locational marginal prices (LMP). The evaluation of the impact of using demand response specific programs on the LMP value supports the manager decision concerning demand response use. The proposed method has been computationally implemented and its application is illustrated in this paper using a 32 bus network with intensive use of distributed generation.

Energy resource scheduling considering reactive power management

The reactive power management is an important task in future power systems. The control of reactive power allows the increase of distributed energy resources penetration as well as the optimal operation of distribution networks. Currently, the control of reactive power is only controlled in large power units and in high and very high voltage substations. In this paper a reactive power control in smart grids paradigm is proposed, considering the management of distributed energy resources and of the distribution network by an aggregator namely Virtual Power Player (VPP).

Short-term scheduling considering five-minute and hour-ahead energy resource management

The introduction of new distributed energy resources, based on natural intermittent power sources, in power systems imposes the development of new adequate operation management and control methods. This paper proposes a short-term Energy Resource Management (ERM) methodology performed in two phases. The first one addresses the hour-ahead ERM scheduling and the second one deals with the five-minute ahead ERM scheduling. Both phases consider the day-ahead resource scheduling solution. The ERM scheduling is formulated as an optimization problem that aims to minimize the operation costs from the point of view of a virtual power player that manages the network and the existing resources. The optimization problem is solved by a deterministic mixed-integer non-linear programming approach and by a heuristic approach based on genetic algorithms. A case study considering a distribution network with 33 bus, 66 distributed generation, 32 loads with demand response contracts and 7 storage units has been implemented in a PSCADbased simulator developed in the field of the presented work, in order to validate the proposed short-term ERM methodology considering the dynamic power system behavior.

A multi-agent based approach for intelligent smart grid management

The spread and globalization of distributed generation (DG) in recent years has should highly influence the changes that occur in Electricity Markets (EMs). DG has brought a large number of new players to take action in the EMs, therefore increasing the complexity of these markets. Simulation based on multi-agent systems appears as a good way of analyzing players’ behavior and interactions, especially in a coalition perspective, and the effects these players have on the markets. MASCEM – Multi-Agent System for Competitive Electricity Markets was created to permit the study of the market operation with several different players and market mechanisms. MASGriP – Multi-Agent Smart Grid Platform is being developed to facilitate the simulation of micro grid (MG) and smart grid (SG) concepts with multiple different scenarios. This paper presents an intelligent management method for MG and SG. The simulation of different methods of control provides an advantage in comparing different possible approaches to respond to market events. Players utilize electric vehicles’ batteries and participate in Demand Response (DR) contracts, taking advantage on the best opportunities brought by the use of all resources, to improve their actions in response to MG and/or SG requests.

Intelligent management of end consumers loads including electric vehicles through a SCADA system

The large penetration of intermittent resources, such as solar and wind generation, involves the use of storage systems in order to improve power system operation. Electric Vehicles (EVs) with gridable capability (V2G) can operate as a means for storing energy. This paper proposes an algorithm to be included in a SCADA (Supervisory Control and Data Acquisition) system, which performs an intelligent management of three types of consumers: domestic, commercial and industrial, that includes the joint management of loads and the charge/discharge of EVs batteries. The proposed methodology has been implemented in a SCADA system developed by the authors of this paper – the SCADA House Intelligent Management (SHIM). Any event in the system, such as a Demand Response (DR) event, triggers the use of an optimization algorithm that performs the optimal energy resources scheduling (including loads and EVs), taking into account the priorities of each load defined by the installation users. A case study considering a specific consumer with several loads and EVs is presented in this paper.