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In this paper, we propose a grapheme verifier based on a neural network (NN) for on-line handwritten Hangul (Korean script) recognition. The verifier complements the global information and discrimination power to hidden Markov models (HMMs) by penalizing incorrect inputs and passing correct ones intact. It takes structural features from partial inputs aligned to HMM states as global information and gives the probability of their validity; one if they belong to the grapheme and zero if they do not. Hangul syllable characters are recognized with the network of grapheme models which are composed of HMM recognizers and NN verifiers. Probabilities of verifiers are integrated with those of HMMs during the search for the most probable path in the network. Experimental result showed that the proposed verifiers effectively reduced recognition errors of HMM recognizers for Hangul syllable characters by 39.2% on average.

Protein-protein binding interaction is the most prevalent biological activity that mediates a great variety of biological processes. The increasing availability of experimental data of protein–protein interaction allows a systematic construction of protein–protein interaction networks, significantly contributing to a better understanding of protein functions and their roles in cellular pathways and human diseases. Compared to well-established classification for protein–protein interactions (PPIs), limited work has been conducted for estimating protein–protein binding free energy, which can provide informative real-value regression models for characterizing the protein–protein binding affinity. In this study, we propose a novel ensemble computational framework, termed ProBAPred (Protein–protein Binding Affinity Predictor), for quantitative estimation of protein–protein binding affinity. A large number of sequence and structural features, including physical–chemical properties, binding energy and conformation annotations, were collected and calculated from currently available protein binding complex datasets and the literature. Feature selection based on the WEKA package was performed to identify and characterize the most informative and contributing feature subsets. Experiments on the independent test showed that our ensemble method achieved the lowest Mean Absolute Error (MAE; 1.657kcal/mol) and the second highest correlation coefficient ($R\text{-value}=0.467$), compared with the existing methods. The datasets and source codes of ProBAPred, and the supplementary materials in this study can be downloaded at http://lightning.med.monash.edu/probapred/ for academic use. We anticipate that the developed ProBAPred regression models can facilitate computational characterization and experimental studies of protein–protein binding affinity.

Tsunami databases contain general information of historical tsunamis, which provides valuable and fundamental data to tsunami-related researches. Nevertheless, before adopting the information stored in various tsunami databases, users should be familiar with these databases’ individual features due to their inhomogeneity in data storage. To this end, a comprehensive review of the worldwide existing tsunami databases was conducted in this study. Two kinds of existing databases are recognized, i.e. the individual database and the organizational database. The individual tsunami databases are generally simple and descriptive with less information about tsunami and associated earthquake. Update of individual database is also out of date. Nevertheless, the organizational tsunami databases, with the retrieval function and timely update, are more sophisticated with significant research orientated information, upon which we can obtain the detailed characteristics of a specified tsunami event. In addition, a further comparison is made between two main organizational databases, i.e. the databases of NTL/WLD and NGDC/WDS. In which, the number of recorded tsunami wave heights per event and the number of definite tsunami events per decade both witness an apparent increase after 1900s, and the landslide-generated tsunamis are found to gradually increase after 1960s. The total tsunami events per decade fluctuate around one hundred since 1990s, whereas the definite tsunami events per decade experience a linear increase in this period. It turns out that though quite a part of the data in NTL/WLD is referred to NGDC/WDS, they do collect different tsunami events, and even for the same event, their recording datasets are still not consistent with each other. Figuring out the structural features of different databases and unveiling their connections and differences facilitate relevant research for disaster prevention and mitigation.