Generation of Semantic Data from Guidelines for Rational Use of Antibiotics

Abstract. Rational use of antibiotics has become an important issue in medical practice and health care. Clinical guidelines are one of the most useful knowledge resources for rational use of antibiotics. As the monitoring of rational use of antibiotics involves complex knowledge of guidelines analysis and management process, traditional way of human intervention is not sufficient to monitor rational use of antibiotics effectively. Therefore, we introduce the semantic technology to semi-automatically transform the knowledge contained in the clinical guidelines and get the semantic data. In this paper, we firstly investigate how to obtain the semantic data from the guidelines knowledge which are described in natural language, then propose an approach to transformation of guidelines knowledge into semantic data, which can be loaded into SeSRUA, a Semantically-Enabled System for Rational Use of Antibiotics. Finally we report how to implement the proposed approach in SToGRUA, a system of Semantic Transformer of guidelines for Rational Use of Antibiotics, as a tool of SeSRUA

Studies on Immune Clonal Selection Algorithm and Application of Bioinformatics

Abstract: Immune algorithms (IAs) are microscopic view of evolutionary algorithms (EAs) and applied in combinatorial optimization problems. This paper addresses to a clonal selection algorithm (CSA) that is one of the most representative IA and was applied into the protein structure prediction (PSP) on AB off-lattice model, in which the memory B cells of the CSA was innovated by employing different strategies: local search and global search in the phase of the mutation. And the CSA was further improved by adding aging operator to combat the premature convergence. However the pure aging operator didn't achieve effective results and sometimes the optimum solution was eliminated. To resolve this problem, the current best solution was reserved by an antibody and it was not eliminated when its age reached its life span. In our experiments the improved algorithm was compared with the standard CSA and the pure aging CSA, which of the results demonstrated that the improved strategy with the memory B cells and long life aging was very effective to overcome premature convergence and to avoid trapped in the local-best solution, and it was also effective in keeping the diversity of the small size population. On the other hand, one novel hybrid algorithm Quantum Immune(QI), which combines Quantum Algorithm (QA) and Immune Clonal Selection(ICS) Algorithm, has been presented for dealing with multi-extremum and multi-parameter problem based on AB off-lattice model in the predicting 2D protein folding structure. Clonal Selection Algorithm was introduced into the hypermutation operators in the Quantum Algorithm to improve the local search ability, and double chains quantum coded was designed to enlarge the probability of the global optimization solution. It showed that the solution mostly trap into the local optimum, to escape the local best solution the aging operator is introduced to improve the performance of the algorithm. Experimental results showed that the lowest energies and computing-time of the improved Quantum Clonal Selection(QCS) algorithm were better than that of the previous methods, and the QCS was further improved by adding aging operator to combat the premature convergence. Compared with previous approaches, the improved QCS algorithm remarkably enhanced the convergence performance and the search efficiency of the immune optimization algorithm

Type Hierarchy Enhanced Event Detection without Triggers

Event detection (ED) aims to detect events from a given text and categorize them into event types. Most of the current approaches to ED rely heavily on the human annotations of triggers, which are often costly and affect the application of ED in other fields. However, triggers are not necessary for the event detection task. We propose a novel framework called Type Hierarchy Enhanced Event Detection Without Triggers (THEED) to avoid this problem. More specifically, We construct a type hierarchy concept module using the external knowledge graph Probase to enhance the semantic representation of event types. In addition, we divide input instances into word-level and context-level representations, which can make the model use different level features. The experimental result indicates that our proposed approach achieves better improvement. Additionally, it is significantly competitive with mainstream trigger-based models

A Secure Storage and Deletion Verification Scheme of Microgrid Data Based on Integrating Blockchain into Edge Computing

A microgrid generates a large amount of power data during daily operation, which needs to be safely transferred, stored, and deleted. In this paper, we propose a secure storage and deletion verification scheme that combines blockchain and edge computing for the problems of limited storage capacity of blockchain and unverifiable data deletion. Firstly, edge computing is used to preprocess power data to reduce the amount of data and to improve the quality of data. Secondly, a hybrid encryption method that combines the improved ElGamal algorithm and the AES-256 algorithm is used to encrypt outsourcing data, and a secure storage chain is built based on the K-Raft consensus protocol to ensure the security of data in the transmission process. Finally, after initiating a data deletion request and successfully deleting the data, a deletion proof is generated and stored in the chain built, based on the Streamlet consensus protocol. The experimental results illustrate that the basic computing cost, block generation time, and communication delay of this scheme are the most efficient; the efficiency of the improved ElGamal algorithm is three times that of the traditional algorithm; the transaction throughput of the the double-layer blockchain can reach 13,000 tps at most. This scheme can realize the safe storage of microgrid data, and can also realize the efficient deletion and verification of outsourcing data

Power-Aware Resource Reconfiguration Using Genetic Algorithm in Cloud Computing

Cloud computing enables scalable computation based on virtualization technology. However, current resource reallocation solution seldom considers the stability of virtual machine (VM) placement pattern. Varied workloads of applications would lead to frequent resource reconfiguration requirements due to repeated appearance of hot nodes. In this paper, several algorithms for VM placement (multiobjective genetic algorithm (MOGA), power-aware multiobjective genetic algorithm (pMOGA), and enhanced power-aware multiobjective genetic algorithm (EpMOGA)) are presented to improve stability of VM placement pattern with less migration overhead. The energy consumption is also considered. A type-matching controller is designed to improve evolution process. Nondominated sorting genetic algorithm II (NSGAII) is used to select new generations during evolution process. Our simulation results demonstrate that these algorithms all provide resource reallocation solutions with long stabilization time of nodes. pMOGA and EpMOGA also better balance the relationship of stabilization and energy efficiency by adding number of active nodes as one of optimal objectives. Type-matching controller makes EpMOGA superior to pMOGA

GridOnto: Knowledge Representation and Extraction for Fault Events in Power Grid

An accurate and comprehensive fault knowledge representation is indispensable for an automated and intelligent processing of power grid failures. Current knowledge graphs are incapable of capturing the complex relations among power grid failures. This paper extends the current knowledge graph representation mechanism through temporal, spatial, and causal representations to facilitate the knowledge representation of power grid faults, allowing for ontology modeling of power event elements and event relationships. During the modeling process, this paper proposes an extraction method which includes Bi-directional Encoder Representation from Transformers (BERT), Bi-directional Long Short-Term Memory (BiLSTM), and Conditional Random Field (CRF) for entities and relationships in power grid faults. The innovation of the method lies in the clever combination of the three, BERT learning semantic representation, BiLSTM further learning semantic features, and CRF joint modeling of labels to improve accuracy, and the results verify the effectiveness and practicality of the method presented in this paper