Managing knowledge flows in Mauritian multinational corporations: empirical analysis using the SECI model

This research was conducted to investigate the management of knowledge flows in a Mauritian multinational organisation. A case study research method was used to gather data which was analysed using the SECI model. Results show that all the four quadrants of this model were applied by the conglomerate in transferring knowledge to its newly acquired manufacturing operations in Madagascar. This paper discusses some of the knowledge management strategies employed.

Equilibrium existence in the circle model with linear quadratic transport cost

We treat the problem of existence of a location-then-price equilibrium in the circle model with a linear quadratic type of transportation cost function which can be either convex or concave. We show the existence of a unique perfect equilibrium for the concave case when the linear and quadratic terms are equal and of a unique perfect equilibrium for the convex case when the linear term is equal to zero. Aside from these two cases, there are feasible locations by the firms for which no equilibrium in the price subgame exists. Finally, we provide a full taxonomy of the price equilibrium regions in terms of weights of the linear and quadratic terms in the cost function.

Good is generally more alike than bad in the information ecology: Investigating the case of (the ABC model of) group stereotypes

On most if not all evaluatively relevant dimensions such as the temperature level, taste intensity, and nutritional value of a meal, one range of adequate, positive states is framed by two ranges of inadequate, negative states, namely too much and too little. This distribution of positive and negative states in the information ecology results in a higher similarity of positive objects, people, and events to other positive stimuli as compared to the similarity of negative stimuli to other negative stimuli. In other words, there are fewer ways in which an object, a person, or an event can be positive as compared to negative. Oftentimes, there is only one way in which a stimulus can be positive (e.g., a good meal has to have an adequate temperature level, taste intensity, and nutritional value). In contrast, there are many different ways in which a stimulus can be negative (e.g., a bad meal can be too hot or too cold, too spicy or too bland, or too fat or too lean). This higher similarity of positive as compared to negative stimuli is important, as similarity greatly impacts speed and accuracy on virtually all levels of information processing, including attention, classification, categorization, judgment and decision making, and recognition and recall memory. Thus, if the difference in similarity between positive and negative stimuli is a general phenomenon, it predicts and may explain a variety of valence asymmetries in cognitive processing (e.g., positive as compared to negative stimuli are processed faster but less accurately). In my dissertation, I show that the similarity asymmetry is indeed a general phenomenon that is observed in thousands of words and pictures. Further, I show that the similarity asymmetry applies to social groups. Groups stereotyped as average on the two dimensions agency / socio-economic success (A) and conservative-progressive beliefs (B) are stereotyped as positive or high on communion (C), while groups stereotyped as extreme on A and B (e.g., managers, homeless people, punks, and religious people) are stereotyped as negative or low on C. As average groups are more similar to one another than extreme groups, according to this ABC model of group stereotypes, positive groups are mentally represented as more similar to one another than negative groups. Finally, I discuss implications of the ABC model of group stereotypes, pointing to avenues for future research on how stereotype content shapes social perception, cognition, and behavior.

Community and hyperlocal journalism: a 'sustainable' model?

In the journalistic ‘field,’ very local news is like a wild pasture—metaphorically speaking. It grows organically, but can be cultivated in certain conditions and harvested for profit. Its diversity and social importance make it a key part of the wider ecology of news (Phillips, 2015). However, across the globe, traditional media companies with interests in this patch are slashing and burning—cutting costs, centralizing production, or even closing down operations. Small start-up news providers are popping up like field mushrooms in their place, wherever conditions seem fertile. And there are also ‘hyperlocal’ and ‘community’ publications more interested in local news subsistence than in making money. This chapter outlines the broad set of economic approaches that have been applied to support community, hyperlocal, and local news and their benefits given the technical, industrial, and cultural changes taking place in the field. We argue the keys to a sustainable future are embracing their diversity and cultivating their ‘niche’: localness.

A clustering-based failure mode and effect analysis model and its application to the edible bird nest industry

Failure mode and effect analysis (FMEA) is a popular safety and reliability analysis tool in examining potential failures of products, process, designs, or services, in a wide range of industries. While FMEA is a popular tool, the limitations of the traditional Risk Priority Number (RPN) model in FMEA have been highlighted in the literature. Even though many alternatives to the traditional RPN model have been proposed, there are not many investigations on the use of clustering techniques in FMEA. The main aim of this paper was to examine the use of a new Euclidean distance-based similarity measure and an incremental-learning clustering model, i.e., fuzzy adaptive resonance theory neural network, for similarity analysis and clustering of failure modes in FMEA; therefore, allowing the failure modes to be analyzed, visualized, and clustered. In this paper, the concept of a risk interval encompassing a group of failure modes is investigated. Besides that, a new approach to analyze risk ordering of different failure groups is introduced. These proposed methods are evaluated using a case study related to the edible bird nest industry in Sarawak, Malaysia. In short, the contributions of this paper are threefold: (1) a new Euclidean distance-based similarity measure, (2) a new risk interval measure for a group of failure modes, and (3) a new analysis of risk ordering of different failure groups.

A model of grain fragmentation based on lattice curvature

A new model is proposed that aims to capture within a single modelling frame all the main microstructural features of a severe plastic deformation process. These are: evolution of the grain size distribution, misorientation distribution, crystallographic texture and the strain-hardening of the material. The model is based on the lattice curvature that develops in all deformed grains. The basic assumption is that lattice rotation within an individual grain is impeded near the grain boundaries by the constraining effects of the neighbouring grains, which gives rise to lattice curvature. On that basis, a fragmentation scheme is developed which is integrated in the Taylor viscoplastic polycrystal model. Dislocation density evolution is traced for each grain, which includes the contribution of geometrically necessary dislocations associated with lattice curvature. The model is applied to equal-channel angular pressing. The role of texture development is shown to be an important element in the grain fragmentation process. Results of this modelling give fairly precise predictions of grain size and grain misorientation distribution. The crystallographic textures are well reproduced and the strength of the material is also reliably predicted based on the modelling of dislocation density evolution coupled with texture development.

Performance analysis of software-defined network switch using M/Geo/1 model

The aim of this letter is to propose an analytical model to study the performance of Software-Defined Network (SDN) switches. Here, SDN switch performance is defined as the time that an SDN switch needs to process packet without the interaction of controller. We exploit the capabilities of queueing theory based M/Geo/1 model to analyze the key factors, flowtable size, packet arrival rate, number of rules, and position of rules. The analytical model is validated using extensive simulations. Our study reveals that these factors have significant influence on the performance of an SDN switch.

Role of ADRI model in teaching and assessing novice programmers

In this research, an ADRI approach was introduced in the teaching and learning process of an introductory programming course. The result shows that the ADRI approach impacted positively on the students learning outcomes. The ADRI approach not only reduced the failure rate but also improved student’s retention in the course.

Design and evaluation of coordinated in-network caching model for content centric networking

In-network caching has been widely adopted in Content Centric Networking (CCN) to accelerate data delivery, mitigate server load and reduce network traffic. However, the line-speed requirement makes the in-network caching space very limited. With the rapid growth of network traffic, it is significant challenging to decide content placement in such limited cache space. To conquer this conflict, coordinated in-network caching schemes are needed so as to maximize the profit of ubiquitous caching capacities. In particular, in-network caching in CCN is deployed as an arbitrary network topology and naturally supports dynamic request routing. Therefore, content placement scheme and dynamic request routing are tightly coupled and should be addressed together. In this paper, we propose a coordinated in-network caching model to decide the optimal content placement and the shortest request routing path under constraints of cache space and link bandwidth in a systematic fashion. Via extensive simulations, the effectiveness and efficiency of our proposed model has been validated.

Deep brain stimulation mediates neurotrophin signaling in an animal model of antidepressant resistance

Chronic adrenocorticotropic hormone administration at circadian nadir produces an antidepressant resistance animal model that does not present with an altered plasma corticosterone profile nor changes in hippocampal brain derived neurotrophic factor and its receptor. Acute and chronic infralimbic deep brain stimulation elicited an antidepressant response in this animal model, which appears to be associated with changes in gene and protein expression of key intracellular mediators of neurotrophic factor signaling. Together, these findings stand to make important positive impacts on treatment strategies for one of the most debilitating and prevalent disorders of the modern era and suggest that antidepressant treatment response may involve mechanisms distinct from chronic stress-mediated induction of depression-like behavioral phenotypes.

Identifying social influence in complex networks: a novel conductance eigenvector centrality model

Identifying influential peers is an important issue for business to promote commercial strategies in social networks. This paper proposes a conductance eigenvector centrality (CEC) model to measure peer influence in the complex social network. The CEC model considers the social network as a conductance network and constructs methods to calculate the conductance matrix of the network. By a novel random walk mechanism, the CEC model obtains stable CEC values which measure the peer influence in the network. The experiments show that the CEC model can achieve robust performance in identifying peer influence. It outperforms the benchmark algorithms and obtains excellent outcomes when the network has high clustering coefficient.

Impact of a modified nursing handover model for improving nursing care and documentation in the emergency department: A pre- and post-implementation study

The aim of this study was to evaluate whether implementation of a new nursing handover model led to improved completion of nursing care activities and documentation. A pre- and post-implementation study, using a survey and document audit, was conducted in a hospital ED in Melbourne. A convenience sample of nurses completed the survey at baseline (n = 67) and post-intervention (n = 59), and the audit was completed at both time points. Results showed significant improvements in several processes: handover in front of the patient (P < 0.001), patients contributed and/or listened to handover discussions (P < 0.001), and provision of adequate information about all patients in the department (P < 0.001). Nurses also reported a reduction in omission of vital signs (P = 0.022) during handover. Three hundred sixty-eight medical records were audited in the two study periods: 173 (pre-intervention) and 195 (post-intervention). Statistically significant improvements in the completion of two nursing care tasks and three documentation items were identified. The findings suggest that implementation of a new handover model improved completion of nursing care activities and documentation, and transfer of important information to nurses on oncoming shifts.

A field model for repairing 3D shapes

This paper proposes a field model for repairing 3D shapes constructed from multi-view RGB data. Specifically, we represent a 3D shape in a Markov random field (MRF) in which the geometric information is encoded by random binary variables and the appearance information is retrieved from a set of RGB images captured at multiple viewpoints. The local priors in the MRF model capture the local structures of object shapes and are learnt from 3D shape templates using a convolutional deep belief network. Repairing a 3D shape is formulated as the maximum a posteriori (MAP) estimation in the corresponding MRF. Variational mean field approximation technique is adopted for the MAP estimation. The proposed method was evaluated on both artificial data and real data obtained from reconstruction of practical scenes. Experimental results have shown the robustness and efficiency of the proposed method in repairing noisy and incomplete 3D shapes.