Although investments of R&D by government and firms have enlarged and the amount of patents has increased rapidly, R&D almost fails to commercialize for various reasons. For the purpose of decreasing failure rate of technology commercialization, it is important to identify emerging business based on technology in advance and establish appropriate strategy, leading to surviving at the market. Therefore, this paper aims to explore emerging Research and Business Development (R&BD) areas, and establish a business strategy based on valuable patents by comprehensively analyzing IPRs – patent as well as design and trademark. First, unrevealed but potential R& BD areas are explored by analyzing the relation between patent and trademark through topic modeling and network analysis, which aims to preferentially find potential business opportunities that can be implemented by new technology. Potential R&BD areas are recognized as the hidden link in the network of patents and trademarks. Second, emerging R&BD areas are selected by considering the status of the competition and markets through trademark analysis based on generative topographic mapping (GTM) after finding potential R&BD areas with network analysis from the viewpoint of the applicant for a trademark. Finally, new opportunities and strategies for successful R&BD are suggested by analyzing design patents that are representative of the appearance of a product in detail. The result of this study provides more concrete R&BD strategies within the framework of product and business development, based on relations between IPRs, which can be regarded as an initial study that comprehensively utilizes diverse kinds of IPRs.

Introduction

The economic paradigm is shifting from an asset-based economy to a knowledge-based economy, which makes intellectual property rights (IPRs) management more important. The IPRs involving patent, design, and trademark serve as the driving force of national and economic development (Alikhan, 2002; Gould and Gruben, 1996). If firms or governments neglect IPRs, this brings about the unnecessary and enormous expense, as well as hindering national development (Kanwar and Evenson, 2003). Among others, the patent has been in the limelight within the framework of technology management, because it contains significant information for decades (Ernst, 2003; Wu et al., 2015). Although the basic function of the patent is to protect the owners’ rights and prevent infringements (Nam and Barnett, 2011), it can be also utilized to monitor competitors, assess technology, examine mergers and acquisitions (M&A) options, and manage human resources, based upon the basic function (Ernst, 2003). The trademark has also received a lot of attention, in that it is able to protect marketing assets within firms against competitors, as well as to link a firm with its customers (Sandner and Block, 2011). In this way, patent and trademark have played important roles in strategic planning and making decisions, and so it is necessary to analyze and manage IPRs comprehensively. As the importance of IP management increases, investments in R&D have rapidly expanded worldwide. While this has caused many patent applications and paper publications, some arguments have been made about the efficiency of R&D investment.

Our understanding of the spatial patterns of landslides in Africa is limited with available landslide studies typically focusing on only one or a few study areas. Moreover, Africa is clearly underrepresented in terms of available landslide inventories. This study aims to produce a first continent-wide landslide susceptibility map for Africa, calibrated with a well-distributed landslide dataset. We reviewed the literature on landslides in Africa and compiled all available landslide inventories (ca. 10,800 landslides), supplemented by additional landslide mapping using Google Earth imagery in underrepresented regions (ca. 7,250 landslides). This resulted in a dataset of approximately 18,050 landslides. Various environmental variables were investigated for their significance in explaining the observed spatial patterns of landslides. To account for potential mapping biases in the dataset, we used Monte Carlo simulations that selected different subsets of mapped landslides to test the significance of the considered environmental variables. Based on these analyses, we constructed two landslide susceptibility maps for Africa: one for all landslide types and one excluding the known rockfalls. In both maps, topography is by far the most significant variable. We evaluated the performance of the fitted multiple logistic regression models using independent subsets of landslides, selected from the total dataset. Overall, both maps perform very well in predicting intra-continental patterns of landslides in Africa and explain about 80% of the observed variance in landslide occurrence. To further test the robustness and sensitivity to mapping biases, we also modelled landslide susceptibility while excluding regions with arid climates, as landslides in these environments are expected to be better preserved over time and therefore likely relatively overrepresented. Despite this potential bias, the effect on the landslide susceptibility model is limited. Based on the constructed database and our analyses we further discuss potential research gaps for landslide prediction in Africa and at continental scales. For example, analysis of the African countries’ mean landslide susceptibility shows a lack of landslide research in various countries prone to landsliding (e.g.: Guinea, Gabon, Lesotho, Madagascar). Apart from the intrinsic value of this landslide susceptibility map as a natural hazard risk management tool, the map and compiled database are highly promising for other applications. For example, we explored the potential significance of landslides as a geomorphic process by confronting our landslide susceptibility map with an available database of measured catchment sediment yield for 500 rivers in Africa. Overall, a significant positive, but relatively weak relation between landslide susceptibility and sediment yield is observed.

Introduction

Studies on landslide risks and fatalities indicate that landslides are present on all continents and are a global threat to humans, infrastructure and the environment (Dilley, 2005; Guzzetti et al., 2012; Haque et al., 2016; Kjekstad & Highland, 2009; Petley, 2012; Sassa & Canuti, 2009; Stanley & Kirschbaum, 2017). While this is certainly also the case for Africa, this continent remains strongly underrepresented in landslide research (e.g. Gariano & Guzzetti, 2016; Kirschbaum et al., 2015, 2010; Maes et al., 2017; Nadim et al., 2006; Petley, 2012; Reichenbach et al., 2018). Also global landslide susceptibility (LSS) maps rely on very few (or no) data of observed landslides in Africa for their calibration (e.g. Hong et al., 2007; Kirschbaum et al., 2009; Nadim et al. 2006; Stanley & Kirschbaum, 2017). Nonetheless, landslides are one of the deadliest natural disasters in Africa (Guha-Sapir et al., 2017). Moreover, their importance and impact are expected to increase due to climate change, with an increase in total precipitation and increasing frequency and intensity of rainstorm events (Gariano & Guzzetti, 2016). This was for instance shown for the Great Lakes region (Shongwe et al., 2011; Souverijns et al., 2016; Thiery et al., 2016), probably one of the most landslide susceptible regions in Africa (Hong et al., 2007; Stanley & Kirschbaum 2017). In addition, Africa is facing a considerable growth in population, which is projected to triple by the end of the 21st century (Gerland et al., 2014). This will likely affect both the frequency and impact of landslide events. For these reasons, earlier studies have urged to fill the gap on landslide research in Africa (e.g. Gariano & Guzzetti, 2016; Jacobs et al., 2016; Maes et al., 2017).

1. INTRODUCTION

2. MATERIAL AND EXPERIMENTAL METHOD

3. Results and discussions

4. CONCLUSION

Acknowledgements

References

The aim of this study is to identify the impact of High Speed Machining defects on the fatigue behaviour of the Al7050 aluminium alloy. A vast experimental campaign under fully reversed plane bending loads containing different surface states has been undertaken to characterize the effect of the surface topography on the fatigue behaviour. The results show that the fatigue strength decreases only when the surface roughness is significantly degraded. It is also pointed out that manual grinding eliminates the effect of the machining defects on the fatigue behaviour. In order to predict the influence of the surface condition on the fatigue behaviour, a numerical approach based on the real surface topology has been developed. It is shown that the numerically identified crack initiation sites are in agreement with the experimental results. A probabilistic approach based on the weakest link concept, associated with the definition of a stress based crack initiation threshold has been integrated in a FE model. This approach leads naturally to a probabilistic Kitagawa type diagram, which in this case explains the relationship between the size defect and the scale effect on the fatigue strength.

INTRODUCTION

This study is part of a French research project that deals with the control of the machining and grinding of large structural aeronautical components. The material under investigation is the 7050-T7451 aluminium alloy. During HSM (High Speed Machining) of aircraft parts, macro-geometrical defects can be created. Mismatch may appear due to the gap left between two consecutive passes of the machining tool. Chatter can also be created due to the tool/part vibrations which may occur during different types of machining. In order to respect the strict specifications imposed by the aeronautical industry, expensive manual grinding is often performed to remove the machining defects. By the local thermomechanical effects induced, machining can change the surface integrity of manufactured parts and therefore change their fatigue strength. Shahzad [1] and Guillemot [2] showed that changing the cutting conditions can generate a change in surface topology and therefore a change in the fatigue life. Furthermore, Shahzad pointed out that the fatigue resistance shows a significant decrease when surface roughness increases. Even if HSM is known to introduce residual stresses in the machined parts, Tang [3] and Rao [4] showed that these residual stresses are mainly located in the first 50 micrometers below the surface. A variation of the microstructure in the upper surface (hardness variation, grain recrystallization) can also be detected after HSM. Campbell [5] showed that for the Al7000, these changes are located in the first 30 micrometers below the surface.