Exploring Potential of REDD+ Readiness with Social Safeguard through Diverse Forest Use Practices in Gunung Palung National Park in West Kalimantan, Indonesia

Implementation is expected to be a measure for sustainable forest management by providing benefit for forest users based on their efforts. Without careful attention for the social safeguard, the mechanism of reducing greenhouse gas emissions from deforestation and forest degradation with forest management (REDD+) might cause negative impact such as depriving of customary forest use rights under unclear tenure and forest use rights typical in Indonesia. This study aimed to explore how REDD+ Safeguard can be applied in readiness activities by analyzing practical forest use situations in a conservation forest, the Gunung Palung National Park as study site. From the results of the questionnaire survey and interviews, characteristics of forest users were identified and compared. The households, mostly Malays, practicing traditional durian collection, were recognized as main forest users depending on on-farm income especially from non-timber forest product (NTFP). Since the income structure is relatively low and unstable, some of them practice farming in forest area or sell their forest use rights to other households. They are inclined to be lack in legal farm land and certain forest use rights. Based on the findings, consideration for diverse forest users and potential readiness activities were discussed and proposed. For achieving REDD+ implementation with sustainable forest management and social safeguard, it will be necessary to respect for customary rights and take comprehensive measures as readiness efforts.

Estimation of Potential GHG Emission Reduction through Corresponded REDD Plus Activities in Remote Area in Central Kalimantan, Indonesia—Case Study in the Paduran Area

REDD plus activities corresponded in Central Kalimantan Province, Indonesia and their GHG emission reductions potential were analyzed. Target area is located in a remote area from Pa-langkaraya, Capital of Central Kalimantan Province and consisted of immigrating people mainly from Java Island. In the target area, most of local people conducted unsustainable land use activities (e.g. slash-and-burn agriculture). From analysis of past land use in the target area, there were drastic changes in land use from 1989 after migration began. Natural secondary forest with high density was greatly reduced (2010 levels are approximately 80% of 1996 levels) and converted to cropland and settlement. Also, the reduction in natural secondary forest with high density allowed Melaleuca cajuputi Powell forest to rapidly increase in size (2010 levels are approximately 3.7 times as 1996 levels). Additionally, as marked point, there was an increase in oil palm plantations from 2008 and onwards. From results of land use change in the past, mean annual GHG emissions of 5450 Gg CO2e year-1 had been continued until year 2010. To consider counter-measure for reducing GHG emissions in the target area, the relationship between past land use changes and human activities was analyzed through workshops with stakeholders of 6 different groups (village authorities, forest fire fighting team, members of farmers group, large landowners, workers outside of village and oil palm plantation and mother having small children). The results of the workshops showed that the core problem of unsustainable land use faced by 4 of the 6 groups of stakeholders was the lack of job opportunities (means to earn a living) in the target area. Also, it was learned that core groups considered oil palm plantations is to alleviate the problem and provide a source of alternative income. Furthermore, the workshops indicated that future land use scenario (reference scenario) will be based on income from oil palm plantations and, to prevent such land conversion, counter-measures (REDD plus project scenario) of indirect activities of local people’s lifestyle improvement (e.g. A new forestry system which uses abundant resources of M. cajuputi forest) and reducing pressures on forest resources should be introduced. This study indicated, by implementing REDD plus project in the target area, potential reduction in GHG emissions is quite large and such GHG reduction will be essential as mitigation activities under the new mitigation mechanism, the Joint Crediting Mechanism (JCM) between Indonesia and Japan.

Evaluation of Appropriate Identification of Deforestation Agents and Drivers for Designing REDD+ Readiness Activities through an Examination of the Area around Gunung Palung National Park, Indonesia

For effective REDD+ implementation with multiple readiness activities, agents and drivers of deforestation and forest degradation needs to be identified appropriately. This study examined how such identification can be utilized for instituting REDD+ activities design. We examined this question by using satellite imagery analysis and socioeconomic surveying around Gunung Palung National Park in Indonesia. After recognizing the deforestation rate in the area, the characteristics of agents and drivers of deforestation were explored by using statistical analysis. Several canonical discriminant analyses revealed that the agents and drivers could be classified effectively by using socioeconomic type rather than ethnic groups or geographical location. A principal component analysis and the associated scatter diagrams showed that various agents and drivers exist in a given area within the study region. Finally, these efforts led to the suggestion of options for REDD+ readiness activities based on the diverse features and underlying causes.

Guest Editorial: Special issue on machine learning and deep learning algorithms for complex networks

In the latest years,researchers from the industry and academia extensively applied machine learning algorithms in a broad range of domains.The goal of this special issue is to illustrate the most recent applications of deep learning methods in a range of real-life domains and to show the practical utility of these techniques.A particular attention goes towards methods to process network data that is capable of modelling complex artificial and natural systems as the interactions of a multitude of simpler entities.

楼梯间内人流合并特性研究(英文)

探讨了楼梯间内人流合并的影响以及随着人员密度的变化,合流方向和楼梯间与大厅之间门开启与关闭状态对人流合并难易程度的影响。研究结果表明:(1)人员密度小时合流较容易;(2)如果加入人流与楼梯内人员疏散方向相同时,人流合并较容易;(3)门处于开启状态时,合流较容易。如果门处于关闭状态,人员的疏散速度将降低30%。

Ageing Society and Assistive Products

North East Asian countries are facing to rapid increase in aged population ratio.The most recent values of aged population ratios are 19.5%,8.7%,and 6.9%,for Japan,Korea and China,respectively.One of the welfare services in the aged society is provision of assistive products.Electronic control systems are commonly adopted in modern assistive products and sensors are indispensable for control units.Alarm systems,such as fire alarm,smoke detectors,and gas leak detectors,have been regarded as indispensable to safety of elderly persons and persons with disability.Main application of chemical sensors in home care of elderly persons is in the field of personal care and personal medical treatment.Products for personal medical treatment include that for medical treatment in home care and that to keep elderly persons healthy.Large market is expected in the latter one.

Adapted REDD plus Based on Local People’s Capabilities: A Case Study from Luang Prabang Province, Lao PDR

The reducing emissions from deforestation and forest degradation (REDD plus) has been proposed as a key tool for reducing greenhouse gases (GHG) from deforestation and forest degradation in land use sector. The development of a REDD plus that considers national, sub-national, and/or local circumstances in relation to a target area requires an analysis of site-specific deforestation drivers and land use characteristics. This study aimed to analyze the capability of forest-dependent people to find the way for reducing pressures on forest resources, i.e. reducing shifting cultivation and identified their patterns of land and forest use, as this knowledge is essential for developing a REDD plus for land and forest management for an area. The study target area was Luang Prabang Province in the northern part of the Lao People’s Democratic Republic (Lao PDR). We conducted questionnaire-based surveys and participatory workshops to identify the drivers of deforestation and forest degradation as well as current local capabilities. Our findings, which focused on the characteristics of ethnic groups (Khmu and Hmong) and the agricultural techniques used by villagers, revealed significant differences between upland rice farmers without paddy fields and farmers with paddy fields who cultivated upland rice in terms of their capabilities to maintain livelihoods. The results of a discriminant function analysis indicated that 66.7% of the initially categorized respondents were correctly classified for the variable Khmu or Hmong and 82.1% classified for the variable upland rice farmers with paddy or upland rice farmers without paddy. The results indicated a lower capability to transition to alternative livelihoods among farmers relying on upland cultivation (i.e. shifting cultivation) than among farmers who cultivate paddy. Moreover, the study revealed the importance of applying a capability approach when planning REDD plus in Lao PDR that account for differential capabilities attributed to ethnicity or other vulnerable group statuses.

SGNR: A Social Graph Neural Network Based Interactive Recommendation Scheme for E-Commerce

Interactive Recommendation(IR)formulates the recommendation as a multi-step decision-making process which can actively utilize the individuals’feedback in multiple steps and optimize the long-term user benefit of recommendation.Deep Reinforcement Learning(DRL)has witnessed great application in IR for ecommerce.However,user cold-start problem impairs the learning process of the DRL-based recommendation scheme.Moreover,most existing DRL-based recommendations ignore user relationships or only consider the single-hop social relationships,which cannot fully utilize the social network.The fact that those schemes can not capture the multiple-hop social relationships among users in IR will result in a sub-optimal recommendation.To address the above issues,this paper proposes a Social Graph Neural network-based interactive Recommendation scheme(SGNR),which is a multiple-hop social relationships enhanced DRL framework.Within this framework,the multiple-hop social relationships among users are extracted from the social network via the graph neural network which can sufficiently take advantage of the social network to provide more personalized recommendations and effectively alleviate the user cold-start problem.The experimental results on two real-world datasets demonstrate that the proposed SGNR outperforms other state-of-the-art DRL-based methods that fail to consider social relationships or only consider single-hop social relationships.

Personalized Real-Time Movie Recommendation System:Practical Prototype and Evaluation

With the eruption of big data,practical recommendation schemes are now very important in various fields,including e-commerce,social networks,and a number of web-based services.Nowadays,there exist many personalized movie recommendation schemes utilizing publicly available movie datasets(e.g.,MovieLens and Netflix),and returning improved performance metrics(e.g.,Root-Mean-Square Error(RMSE)).However,two fundamental issues faced by movie recommendation systems are still neglected:first,scalability,and second,practical usage feedback and verification based on real implementation.In particular,Collaborative Filtering(CF)is one of the major prevailing techniques for implementing recommendation systems.However,traditional CF schemes suffer from a time complexity problem,which makes them bad candidates for real-world recommendation systems.In this paper,we address these two issues.Firstly,a simple but high-efficient recommendation algorithm is proposed,which exploits users1 profile attributes to partition them into several clusters.For each cluster,a virtual opinion leader is conceived to represent the whole cluster,such that the dimension of the original useritem matrix can be significantly reduced,then a Weighted Slope One-VU method is designed and applied to the virtual opinion leader-item matrix to obtain the recommendation results.Compared to traditional clusteringbased CF recommendation schemes,our method can significantly reduce the time complexity,while achieving comparable recommendation performance.Furthermore,we have constructed a real personalized web-based movie recommendation system,MovieWatch,opened it to the public,collected user feedback on recommendations,and evaluated the feasibility and accuracy of our system based on this real-world data.