Scenario Analysis on Greenhouse Gas Emission for Waste-to-Energy Alternatives in Japan

This study focuses on Greenhouse Gas (GHG) emissions and reductions of Municipal Solid Waste (MSW) incineration. The authors aim to estimate the detailed composition of GHG emissions and reductions from the waste incineration facility and their influence factors using two Japanese databases on the operation of incinerators from Japan Ministry of the Environment (1,243 facilities) and Japan Waste Research Foundation (814 facilities). The databases cover detailed data on MSW amount and characteristics, specifications of the facility, annual utility consumption, and annual energy/material recovery. The authors analyze the correlations among them and develop predictive models for the detailed components of GHG emissions and reductions.Japan Ministry of the Environment intended to group small municipalities for replacing small-scale incinerators to large-scale Waste-to-Energy (WtE) facilities with a higher energy recovery efficiency. Based on the abovementioned data and models, the authors estimate the expected effects of the block formation and major technological alternatives for GHG mitigation by the national level.The current net GHG emission rate from 1,243 operating waste incineration plants in Japan in 2009 was estimated to be 653 kgCO2e/t. In the block formation, 1,007 plants were assumed to be closed; 236 kept operating; and 286 facilities would be newly built. The net GHG emission rate could be cut off to 454 kgCO2e/t by applying the block formation and technological alternatives with a higher energy recovery efficiency (stalker furnace with power generation by extraction condensing turbine providing steam higher than 3MPa and 300 C). Ash melting caused a larger GHG emission by the increase in energy consumption. The GHG reduction by slag recycling was limited.Furthermore, the net GHG emission rate could be reduced to 242 kgCO2e/t by applying the Best Available Technique (BAT) for combined heat and power plants. When compared with the current status, BAT can reduce 185 kgCO2e/t by improving the power generation efficiency and 187 kgCO2e/t by expanding heat utilization. At present, heat utilization is very limited in Japan, but heat utilization should be more focused and promoted for GHG mitigation decisions

Viscosity solutions to parabolic complex Monge-Amp\`ere equations

In this paper, we study the Cauchy-Dirichlet problem for Parabolic complex Monge-Amp\`ere equations on a strongly pseudoconvex domain by the viscosity method. We extend the results in [EGZ15b] on the existence of solution and the convergence at infinity. We also establish the H\"older regularity of the solutions when the Cauchy-Dirichlet data are H\"older continuous.Comment: 35 pages. arXiv admin note: text overlap with arXiv:1407.2494 by other author

Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs

Background: MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model. Results: Instead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of "disease modules" in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable, performing well when using both experimentally validated and predicted miRNA-target gene interaction data for network construction. Finally, using RWRMTN, we identified 76 novel miRNAs associated with 23 disease phenotypes which were present in a recent database of known disease-miRNA associations. Conclusions: Summarizing, using random walks on mutual miRNA-target networks improves the prediction of novel disease-associated miRNAs because of the existence of "disease modules" in these networks

ASPER: Answer Set Programming Enhanced Neural Network Models for Joint Entity-Relation Extraction

A plethora of approaches have been proposed for joint entity-relation (ER) extraction. Most of these methods largely depend on a large amount of manually annotated training data. However, manual data annotation is time consuming, labor intensive, and error prone. Human beings learn using both data (through induction) and knowledge (through deduction). Answer Set Programming (ASP) has been a widely utilized approach for knowledge representation and reasoning that is elaboration tolerant and adept at reasoning with incomplete information. This paper proposes a new approach, ASP-enhanced Entity-Relation extraction (ASPER), to jointly recognize entities and relations by learning from both data and domain knowledge. In particular, ASPER takes advantage of the factual knowledge (represented as facts in ASP) and derived knowledge (represented as rules in ASP) in the learning process of neural network models. We have conducted experiments on two real datasets and compare our method with three baselines. The results show that our ASPER model consistently outperforms the baselines

δ-equality of intuitionistic fuzzy sets: a new proximity measure and applications in medical diagnosis

Intuitionistic fuzzy set is capable of handling uncertainty with counterpart falsities which exist in nature. Proximity measure is a convenient way to demonstrate impractical significance of values of memberships in the intuitionistic fuzzy set. However, the related works of Pappis (Fuzzy Sets Syst 39(1):111–115, 1991), Hong and Hwang (Fuzzy Sets Syst 66(3):383–386, 1994), Virant (2000) and Cai (IEEE Trans Fuzzy Syst 9(5):738–750, 2001) did not model the measure in the context of the intuitionistic fuzzy set but in the Zadeh’s fuzzy set instead. In this paper, we examine this problem and propose new notions of δ-equalities for the intuitionistic fuzzy set and δ-equalities for intuitionistic fuzzy relations. Two fuzzy sets are said to be δ-equal if they are equal to an extent of δ. The applications of δ-equalities are important to fuzzy statistics and fuzzy reasoning. Several characteristics of δ-equalities that were not discussed in the previous works are also investigated. We apply the δ-equalities to the application of medical diagnosis to investigate a patient’s diseases from symptoms. The idea is using δ-equalities for intuitionistic fuzzy relations to find groups of intuitionistic fuzzified set with certain equality or similar degrees then combining them. Numerical examples are given to illustrate validity of the proposed algorithm. Further, we conduct experiments on real medical datasets to check the efficiency and applicability on real-world problems. The results obtained are also better in comparison with 10 existing diagnosis methods namely De et al. (Fuzzy Sets Syst 117:209–213, 2001), Samuel and Balamurugan (Appl Math Sci 6(35):1741–1746, 2012), Szmidt and Kacprzyk (2004), Zhang et al. (Procedia Eng 29:4336–4342, 2012), Hung and Yang (Pattern Recogn Lett 25:1603–1611, 2004), Wang and Xin (Pattern Recogn Lett 26:2063–2069, 2005), Vlachos and Sergiadis (Pattern Recogn Lett 28(2):197– 206, 2007), Zhang and Jiang (Inf Sci 178(6):4184–4191, 2008), Maheshwari and Srivastava (J Appl Anal Comput 6(3):772–789, 2016) and Support Vector Machine (SVM)

Current status and behavior modeling on household solid-waste separation: a case study in Da Nang city, Vietnam

This study focused on household solid-waste recycling in Da Nang city, Vietnam to assess the existing separation behavior and clarify the factors influencing the separation behavior. The authors conducted a questionnaire survey for 150 households in 6 urban districts, which consisted of household attributes, separation behavior, and the household's attitude on recycling and the environment. The waste separation rates were determined for leftover food and 13 recyclable items and the recyclable disposal habit was also assessed. The separation rate of leftover food was 77.3%. Among 13 surveyed recyclable items, plastic bottles and metal cans were two popular items with higher separation rate (72.5% and 63.8%, respectively). To identify the conscious structure and determinants of separation behavior, the authors developed a predictive model on the separation behavior of leftover food and recyclables by logistic and multiple linear regression analyses. The positive factors included behavior intention, sympathy for the collector, incentive brought by recycling, goal intention, internal norm, and perception of responsibility and seriousness. The negative factor was evaluation of trouble. The authors also analyzed the differences in separation rates among attributes. Based on the significant influence factors and attributes, the authors suggested how to promote separation behavior