How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

Knowledge graph completion aims to perform link prediction between entities. In this paper, we consider the approach of knowledge graph embeddings. Recently, models such as TransE and TransH build entity and relation embeddings by regarding a relation as translation from head entity to tail entity. We note that these models simply put both entities and relations within the same semantic space. In fact, an entity may have multiple aspects and various relations may focus on different aspects of entities, which makes a common space insufficient for modeling. In this paper, we propose TransR to build entity and relation embeddings in separate entity space and relation spaces. Afterwards, we learn embeddings by first projecting entities from entity space to corresponding relation space and then building translations between projected entities. In experiments, we evaluate our models on three tasks including link prediction, triple classification and relational fact extraction. Experimental results show significant and consistent improvements compared to state-of-the-art baselines including TransE and TransH. The source code of this paper can be obtained from https://github.com/mrlyk423/relation_extraction.

/pdf/learning-entity-and-relation-embeddings-for-knowledge-graph-4llsa9078e.pdf

Learning entity and relation embeddings for knowledge graph completion

Better stewardship of land is needed to achieve the Paris Climate Agreement goal of holding warming to below 2 °C; however, confusion persists about the specific set of land stewardship options available and their mitigation potential. To address this, we identify and quantify "natural climate solutions" (NCS): 20 conservation, restoration, and improved land management actions that increase carbon storage and/or avoid greenhouse gas emissions across global forests, wetlands, grasslands, and agricultural lands. We find that the maximum potential of NCS-when constrained by food security, fiber security, and biodiversity conservation-is 23.8 petagrams of CO2 equivalent (PgCO2e) y-1 (95% CI 20.3-37.4). This is ≥30% higher than prior estimates, which did not include the full range of options and safeguards considered here. About half of this maximum (11.3 PgCO2e y-1) represents cost-effective climate mitigation, assuming the social cost of CO2 pollution is ≥100 USD MgCO2e-1 by 2030. Natural climate solutions can provide 37% of cost-effective CO2 mitigation needed through 2030 for a >66% chance of holding warming to below 2 °C. One-third of this cost-effective NCS mitigation can be delivered at or below 10 USD MgCO2-1 Most NCS actions-if effectively implemented-also offer water filtration, flood buffering, soil health, biodiversity habitat, and enhanced climate resilience. Work remains to better constrain uncertainty of NCS mitigation estimates. Nevertheless, existing knowledge reported here provides a robust basis for immediate global action to improve ecosystem stewardship as a major solution to climate change.

https://www.pnas.org/content/pnas/114/44/11645.full.pdf

Natural climate solutions

It has been frequently stated, but without provision of supporting evidence, that the world has lost 50% of its wetlands (or 50% since 1900 AD). This review of 189 reports of change in wetland area finds that the reported long-term loss of natural wetlands averages between 54–57% but loss may have been as high as 87% since 1700 AD. There has been a much (3.7 times) faster rate of wetland loss during the 20th and early 21st centuries, with a loss of 64–71% of wetlands since 1900 AD. Losses have been larger and faster for inland than coastal natural wetlands. Although the rate of wetland loss in Europe has slowed, and in North America has remained low since the 1980s, the rate has remained high in Asia, where large-scale and rapid conversion of coastal and inland natural wetlands is continuing. It is unclear whether the investment by national governments in the Ramsar Convention on Wetlands has influenced these rates of loss. There is a need to improve the knowledge of change in wetland areas worldwide, particularly for Africa, the Neotropics and Oceania, and to improve the consistency of data on change in wetland areas in published papers and reports.

How much wetland has the world lost? Long-term and recent trends in global wetland area

Accurate inventory of tropical peatland is important in order to (a) determine the magnitude of the carbon pool; (b) estimate the scale of transfers of peat-derived greenhouse gases to the atmosphere resulting from land use change; and (c) support carbon emissions reduction policies. We review available information on tropical peatland area and thickness and calculate peat volume and carbon content in order to determine their best estimates and ranges of variation. Our best estimate of tropical peatland area is 441 025 km2 (?11% of global peatland area) of which 247 778 km2 (56%) is in Southeast Asia. We estimate the volume of tropical peat to be 1758 Gm3 (?18–25% of global peat volume) with 1359 Gm3 in Southeast Asia (77% of all tropical peat). This new assessment reveals a larger tropical peatland carbon pool than previous estimates, with a best estimate of 88.6 Gt (range 81.7–91.9 Gt) equal to 15–19% of the global peat carbon pool. Of this, 68.5 Gt (77%) is in Southeast Asia, equal to 11–14% of global peat carbon. A single country, Indonesia, has the largest share of tropical peat carbon (57.4 Gt, 65%), followed by Malaysia (9.1 Gt, 10%). These data are used to provide revised estimates for Indonesian and Malaysian forest soil carbon pools of 77 and 15 Gt, respectively, and total forest carbon pools (biomass plus soil) of 97 and 19 Gt. Peat carbon contributes 60% to the total forest soil carbon pool in Malaysia and 74% in Indonesia. These results emphasize the prominent global and regional roles played by the tropical peat carbon pool and the importance of including this pool in national and regional assessments of terrestrial carbon stocks and the prediction of peat-derived greenhouse gas emissions.

https://hal.archives-ouvertes.fr/hal-00599518/document

Global and regional importance of the tropical peatland carbon pool

Subsurface drainage practices: From manual installation to large-scale implementation

Frequency and regression analysis.

Water in the unsaturated zone.

Subsurface flow to drains

Participatory modelling has provided a new approach to overcome the problem of data scarcity which formerly interfered with the environmental planning for the restoration of the Kolleru-Upputeru wetland ecosystem on the east coast of Andhra Pradesh in South India. New ways had to be found to address the shortcomings of traditionally validated simulation models. The traditional validation process was replaced by joint plausibility discussions and shared vision building in order to improve the understanding of cause-effect relationships and proposals for restoration measures. This study has aimed to match the tacit knowledge of the local stakeholders with explicit scientific knowledge in order to create (i) a mutual basis for an integrated approach as opposed to single-issue measures and (ii) a mutual agreement on follow-up steps needed to sustain both the livelihood of the people as well as the wetland ecosystem. The challenge was to address the hydrological and social complexity. On the basis of a literature review, input data for model simulations were generated from the location-specific knowledge of stakeholders and a rapid field appraisal. The model simulations were used to predict the effects of a number of restoration options. In two workshops, these restoration options were discussed with the stakeholders in order to improve the mutual understanding of the complexity of the wetland system and to reach an agreement on the outlines of an integrated action plan. The participatory modelling approach proved to be a useful tool to obtain a consensus of opinions among the stakeholders.

Henk Ritzema

Papers

Subsurface drainage practices: From manual installation to large-scale implementation

Frequency and regression analysis.

Water in the unsaturated zone.

Subsurface flow to drains

Using participatory modelling to compensate for data scarcity in environmental planning: A case study from India