Concordia University

About: Concordia University is a education organization based out in Montreal, Quebec, Canada. It is known for research contribution in the topics: Control theory & Population. The organization has 13565 authors who have published 31084 publications receiving 783525 citations. The organization is also known as: Sir George Williams University & Loyola College, Montreal.

Strategic organizational change: the role of leadership, learning, motivation and productivity

Abstract: Presents an overview of strategic organizational change (SOC) and its managerial impact on leadership, learning, motivation and productivity. Theoretical and empirical data presented are: the sources and determinants of strategic organizational change; the management implications of SOC; organizational leadership within the context of SOC; learning aspects of SOC; the impact of SOC on organizational and individual productivity; a model that explains the relationships between SOC, leadership, learning, motivation and productivity. Depicts strategic organizational change as an integrative process with all organizational elements such as human resources, systems and technologies being considered for successful change to occur. The proposed model for strategic organizational change is an attempt to link the software and hardware components of organizations. In view of the pressures being expected from the external environment and the critical vision of organizations, research suggests that top management needs to establish a flexible and adaptive infrastructure that should lead contemporary and complex organizations to optimum levels of performance. The largest barrier to “change” is not changes to technologies and work processes but changes involving people.

Fuzzy Approximation-Based Adaptive Backstepping Control of an Exoskeleton for Human Upper Limbs

Abstract: This paper presents fuzzy approximation-based adaptive backstepping control of an exoskeleton for human upper limbs to provide forearm movement assistance so that a human forearm can track any continuous desired trajectory (or constant setpoint) in the presence of parametric/functional uncertainties, unmodeled dynamics, actuator dynamics, and/or disturbances from environments. Given the desired trajectories of human forearm positions, in the developed control, adaptive fuzzy approximators are used to estimate the dynamical uncertainties of the human–robot system, and an iterative learning scheme is utilized to compensate for unknown time-varying periodic disturbances. With the synthesis of the backstepping, iterative learning, and Lyapunov function approaches, the developed controller does not require exact knowledge of the exoskeleton model, and the close-loop system can be proven to be semiglobally uniformly bounded. Three comparison experiments are conducted to illustrate the effectiveness of the proposed control scheme by tracking periodic/repeated trajectories.

Direct Discovery of High Utility Itemsets without Candidate Generation

Abstract: Utility mining emerged recently to address the limitation of frequent itemset mining by introducing interestingness measures that reflect both the statistical significance and the user's expectation. Among utility mining problems, utility mining with the itemset share framework is a hard one as no anti-monotone property holds with the interestingness measure. The state-of-the-art works on this problem all employ a two-phase, candidate generation approach, which suffers from the scalability issue due to the huge number of candidates. This paper proposes a high utility itemset growth approach that works in a single phase without generating candidates. Our basic approach is to enumerate itemsets by prefix extensions, to prune search space by utility upper bounding, and to maintain original utility information in the mining process by a novel data structure. Such a data structure enables us to compute a tight bound for powerful pruning and to directly identify high utility itemsets in an efficient and scalable way. We further enhance the efficiency significantly by introducing recursive irrelevant item filtering with sparse data, and a lookahead strategy with dense data. Extensive experiments on sparse and dense, synthetic and real data suggest that our algorithm outperforms the state-of-the-art algorithms over one order of magnitude.

LEUCINE-RESPONSIVE REGULATORY PROTEIN: A Global Regulator of Gene Expression in E. Coli

Abstract: The leucine-responsive regulatory protein (Lrp) regulates transcription of the many genes of the Lrp regulon, repressing some and activating others, some in response to L-leucine and some independent of it. The physiology and molecular biology of the regulon in Escherichia coli are summarized here. However, the high degree of conservation of the protein suggests that it has an important role in all enterobacteria. We suggest that this role is not only as a transcriptional regulator but also as a determinant of chromosome structure.