Clustering under the line graph transformation: application to reaction network

BACKGROUND: Many real networks can be understood as two complementary networks with two kind of nodes. This is the case of metabolic networks where the first network has chemical compounds as nodes and the second one has nodes as reactions. In general, the second network may be related to the first one by a technique called line graph transformation (i.e., edges in an initial network are transformed into nodes). Recently, the main topological properties of the metabolic networks have been properly described by means of a hierarchical model. While the chemical compound network has been classified as hierarchical network, a detailed study of the chemical reaction network had not been carried out. RESULTS: We have applied the line graph transformation to a hierarchical network and the degree-dependent clustering coefficient C(k) is calculated for the transformed network. C(k) indicates the probability that two nearest neighbours of a vertex of degree k are connected to each other. While C(k) follows the scaling law C(k) ~ k(-1.1 )for the initial hierarchical network, C(k) scales weakly as k(0.08 )for the transformed network. This theoretical prediction was compared with the experimental data of chemical reactions from the KEGG database finding a good agreement. CONCLUSIONS: The weak scaling found for the transformed network indicates that the reaction network can be identified as a degree-independent clustering network. By using this result, the hierarchical classification of the reaction network is discussed

VisANT: data-integrating visual framework for biological networks and modules

VisANT is a web-based software framework for visualizing and analyzing many types of networks of biological interactions and associations. Networks are a useful computational tool for representing many types of biological data, such as biomolecular interactions, cellular pathways and functional modules. Given user-defined sets of interactions or groupings between genes or proteins, VisANT provides: (i) a visual interface for combining and annotating network data, (ii) supporting function and annotation data for different genomes from the Gene Ontology and KEGG databases and (iii) the statistical and analytical tools needed for extracting topological properties of the user-defined networks. Users can customize, modify, save and share network views with other users, and import basic network data representations from their own data sources, and from standard exchange formats such as PSI-MI and BioPAX. The software framework we employ also supports the development of more sophisticated visualization and analysis functions through its open API for Java-based plug-ins. VisANT is distributed freely via the web at and can also be downloaded for individual use

VisANT: data-integrating visual framework for biological networks and modules

VisANT is a web-based software framework for visualizing and analyzing many types of networks of biological interactions and associations. Networks are a useful computational tool for representing many types of biological data, such as biomolecular interactions, cellular pathways and functional modules. Given user-defined sets of interactions or groupings between genes or proteins, VisANT provides: (i) a visual interface for combining and annotating network data, (ii) supporting function and annotation data for different genomes from the Gene Ontology and KEGG databases and (iii) the statistical and analytical tools needed for extracting topological properties of the user-defined networks. Users can customize, modify, save and share network views with other users, and import basic network data representations from their own data sources, and from standard exchange formats such as PSI-MI and BioPAX. The software framework we employ also supports the development of more sophisticated visualization and analysis functions through its open API for Java-based plug-ins. VisANT is distributed freely via the web at and can also be downloaded for individual use

Open Multi-Access Network Platform with Dynamic Task Offloading and Intelligent Resource Monitoring

We constructed an open multi-access network platform using open-source hardware and software. The open multi-access network platform is characterized by the flexible utilization of network functions, integral management and control of wired and wireless access networks, zero-touch provisioning, intelligent resource monitoring, and dynamic task offloading. We also propose an application-driven dynamic task offloading that utilizes intelligent resource monitoring to ensure effective task processing in edge and cloud servers. For this purpose, we developed a mobile application and server applications for the open multi-access network platform. To investigate the feasibility and availability of our developed platform, we experimentally and analytically evaluated the effectiveness of application-driven dynamic task offloading and intelligent resource monitoring. The experimental results demonstrated that application-driven dynamic task offloading could reduce real-time task response time and traffic over metro and core networks

Hedging strategies for solar power businesses in electricity market using weather derivatives

金沢大学融合研究域融合科学系The large-scale introduction of natural energy being promoted worldwide in recent years leads to an increased impact of weather fluctuations on wholesale electricity prices. In markets where the introduction of solar power generation is rapidly progressing worldwide including Japan, hedging needs for revenue fluctuations in the solar power business have been expanding year by year. Therefore, this study proposes hedging strategies for the revenue of power generation companies that trade generated solar power through the wholesale electricity market, using a portfolio of derivatives whose underlying assets consist of fuel price, solar radiation, and temperature. We specifically propose a multilateral hedging method that applies multiple non-parametric regression methods such as tensor product spline function, ANOVA decomposition, and spline function with cross variable, and demonstrate the hedging effect using empirical data from the Japan Electric Power Exchange (JEPX). © 2019 IEEE

Cross Hedging Using Prediction Error Weather Derivatives for Loss of Solar Output Prediction Errors in Electricity Market

金沢大学融合研究域融合科学系Predicting future solar conditions is important for electricity industries with solar power generators to quote a day-ahead sales contract in the electricity market. If a prediction error exists, the market-monitoring agent has to prepare another power generation resource to immediately compensate for the shortage, resulting in an additional cost. In this context, a penalty may be required depending on the size of the prediction error, which may lead to a significant loss for solar power producers. Because the main source of such losses is from prediction errors of solar conditions, they can instead effectively utilize a derivative contract based on solar prediction errors. The objective of this work is to provide such a derivative contract, namely, a prediction error weather derivative. First, defining a certain loss function, we measure the hedge effect of the derivative on solar radiation prediction error, thereby verifying that the existing hedging method for wind power can also be applied to solar power generation with periodic trends. By introducing the temperature derivative on the absolute prediction error, we also propose a cross-hedging method, where we demonstrate not only a further variance reduction effect when used with solar radiation derivatives, but also a certain hedge effect obtained even when only the temperature derivative is used. For temperature derivative pricing and optimal contract volume estimation, we propose a method using a tensor-product spline function that simultaneously incorporates the smoothing conditions of both the direction of intraday time trend and seasonal trend, and consequently verify its effectiveness. © 2018, Springer Japan KK, part of Springer Nature.Embargo Period 12 month

Pembelajaran Berbasis Projek dengan Pendekatan Jelajah Alam Sekitar sebagai Model Perkuliahan Fisiologi Hewan

Penelitian ini dilakukan dengan tujuan untuk menguji efektivitas penerapan model pembelajaran berbasis projek (Project Based Learning/PBL) dengan pendekatan Jelajah Alam Sekitar (JAS) pada perkuliahan Fisiologi Hewan bagi mahasiswa Program Studi Pendidikan Biologi. Penelitian ini merupakan quasi eksperimen dengan desain one shot case study. Populasi dalam penelitian ini adalah mahasiswa Program Studi Pendidikan Biologi dan sebagai sampel adalah mahasiswa semester 4 Prodi Pendidikan Biologi rombel 1,2,dan 3 tahun ajaran 2010 / 2011 yang mengambil mata kuliah Fisiologi Hewan. Pengambilan sampel dengan teknik purposive random sampling. Sebagai variabel bebas adalah penerapan model PBL dengan pendekatan JAS, sedangkan sebagai variabel terikat adalah efektivitas model pembelajaran yang diterapkan dilihat dari hasil belajar, aktivitas siswa dan keterlaksanaan kegiatan yang diprogramkan. Sumber data penelitian adalah mahasiswa. Data yang diambil adalah nilai ujian tulis, nilai laporan, nilai presentasi , aktivitas mahasiswa serta tanggapan keterlaksanaan PBL dengan pendekatan JAS. Hasil penelitian menunjukkan bahwa penerapan model PBL dengan pendekatan JAS telah dapat mencapai indikator-indikator yang ditetapkan yaitu mahasiswa yang memperoleh nilai minimal B mencapai 70%, tanpa nilai D dan E, mahasiswa dengan kriteria keaktifan pada kategori tinggi dan sangat tinggi mencapai minimal 80%, dan tingkat keterlaksanaan kegiatan dalam pembelajaran berbasis projek dengan pendekatan JAS mencapai 80%. Simpulan yang dapat diambil dari penelitian ini adalah model pembelajaran berbasis projek dengan pendekatan JAS pada perkuliahan Fisiologi Hewan efektif diterapkan