Document generality: its computation for ranking

The increased variety of information makes it critical to retrieve documents which are not only relevant but also broad enough to cover as many different aspects of a certain topic as possible. The increased variety of users also makes it critical to retrieve documents that are jargon free and easy-to-understand rather than the specific technical materials. In this paper, we propose a new concept namely document generality computation. Generality of document is of fundamental importance to information retrieval. Document generality is the state or quality of docu- ment being general. We compute document general- ity based on a domain-ontology method that analyzes scope and semantic cohesion of concepts appeared in the text. For test purposes, our proposed approach is then applied to improving the performance of doc- ument ranking in bio-medical information retrieval. The retrieved documents are re-ranked by a combined score of similarity and the closeness of documents’ generality to that of a query. The experiments have shown that our method can work on a large scale bio-medical text corpus OHSUMED (Hersh, Buckley, Leone & Hickam 1994), which is a subset of MEDLINE collection containing of 348,566 medical journal references and 101 test queries, with an encouraging performance

Epistemic Uncertainty Quantification of Seismic Damage Assessment

The damage-based structural seismic performance evaluations are widely used in seismic design and risk evaluation of civil facilities. Due to the large uncertainties rooted in this procedure, the application of damage quantification results is still a challenge for researchers and engineers. Uncertainties in damage assessment procedure are important consideration in performance evaluation and design of structures against earthquakes. Due to lack of knowledge or incomplete, inaccurate, unclear information in the modeling, simulation, and design, there are limitations in using only one framework (probability theory) to quantify uncertainty in a system because of the impreciseness of data or knowledge. In this work, a methodology based on the evidence theory is presented for quantifying the epistemic uncertainty of damage assessment procedure. The proposed methodology is applied to seismic damage assessment procedure while considering various sources of uncertainty emanating from experimental force-displacement data of reinforced concrete column. In order to alleviate the computational difficulties in the evidence theory-based uncertainty quantification analysis (UQ), a differential evolution-based computational strategy for efficient calculation of the propagated belief structure in a system with evidence theory is presented here. Finally, a seismic damage assessment example is investigated to demonstrate the effectiveness of the proposed method

A Vehicular Trust Blockchain Framework with Scalable Byzantine Consensus

The maturing blockchain technology has gradually promoted decentralized data storage from cryptocurrencies to other applications, such as trust management, resulting in new challenges based on specific scenarios. Taking the mobile trust blockchain within a vehicular network as an example, many users require the system to process massive traffic information for accurate trust assessment, preserve data reliably, and respond quickly. While existing vehicular blockchain systems ensure immutability, transparency, and traceability, they are limited in terms of scalability, performance, and security. To address these issues, this paper proposes a novel decentralized vehicle trust management solution and a well-matched blockchain framework that provides both security and performance. The paper primarily addresses two issues: i) To provide accurate trust evaluation, the trust model adopts a decentralized and peer-review-based trust computation method secured by trusted execution environments (TEEs). ii) To ensure reliable trust management, a multi-shard blockchain framework is developed with a novel hierarchical Byzantine consensus protocol, improving efficiency and security while providing high scalability and performance. The proposed scheme combines the decentralized trust model with a multi-shard blockchain, preserving trust information through a hierarchical consensus protocol. Finally, real-world experiments are conducted by developing a testbed deployed on both local and cloud servers for performance measurements

Predicting Bus Travel Time with Hybrid Incomplete Data – A Deep Learning Approach

The application of predicting bus travel time with real-time information, including Global Positioning System (GPS) and Electronic Smart Card (ESC) data is effective to advance the level of service by reducing wait time and improving schedule adherence. However, missing information in the data stream is inevitable for various reasons, which may seriously affect prediction accuracy. To address this problem, this research proposes a Long Short-Term Memory (LSTM) model to predict bus travel time, considering incomplete data. To improve the model performance in terms of accuracy and efficiency, a Genetic Algorithm (GA) is developed and applied to optimise hyperparameters of the LSTM model. The model performance is assessed by simulation and real-world data. The results suggest that the proposed approach with hybrid data outperforms the approaches with ESC and GPS data individually. With GA, the proposed model outperforms the traditional one in terms of lower Root Mean Square Error (RMSE). The prediction accuracy with various combinations of ESC and GPS data is assessed. The results can serve as a guideline for transit agencies to deploy GPS devices in a bus fleet considering the market penetration of ESC

Genetic Analysis and Molecular Breeding Applications of Malting Quality QTLs in Barley

Malting quality is an important determinant of the value of barley grain used in malting and brewing. With recent sequencing and assembling of the barley genome, an increasing number of quantitative trait loci (QTLs) and genes related to malting quality have been identified and cloned, which lays a good molecular genetic basis for barley quality improvement. In this review, we describe the following indicators of malting quality: malt extract (ME), diastatic power (DP), kolbach index (KI), wort viscosity (VIS), free amino nitrogen (FAN) content, soluble protein (SP) content, wort β-glucan (WBG) content, and protein content (PC), and have list related QTLs/genes with high phenotypic variation in multiple populations or environments. Meanwhile, the correlations among the quality parameters and parts of significant indicators suitable for improvement are discussed based on nutrient composition and content required for high-quality malt, which will provide reference for molecular marker-assisted selection (MAS) of malting quality in barley

Molecular evolution of calcium signaling and transport in plant adaptation to abiotic stress

Adaptation to unfavorable abiotic stresses is one of the key processes in the evolution of plants. Calcium (Ca2+) signaling is characterized by the spatiotemporal pattern of Ca2+ distribution and the activities of multi-domain proteins in integrating environmental stimuli and cellular responses, which are crucial early events in abiotic stress responses in plants. However, a comprehensive summary and explanation for evolutionary and functional synergies in Ca2+ signaling remains elusive in green plants. We review mechanisms of Ca2+ membrane transporters and intracellular Ca2+ sensors with evolutionary imprinting and structural clues. These may provide molecular and bioinformatics insights for the functional analysis of some non-model species in the evolutionarily important green plant lineages. We summarize the chronological order, spatial location, and characteristics of Ca2+ functional proteins. Furthermore, we highlight the integral functions of calcium-signaling components in various nodes of the Ca2+ signaling pathway through conserved or variant evolutionary processes. These ultimately bridge the Ca2+ cascade reactions into regulatory networks, particularly in the hormonal signaling pathways. In summary, this review provides new perspectives towards a better understanding of the evolution, interaction and integration of Ca2+ signaling components in green plants, which is likely to benefit future research in agriculture, evolutionary biology, ecology and the environment

STAT1 as a downstream mediator of ERK signaling contributes to bone cancer pain by regulating MHC II expression in spinal microglia

Major histocompatibility class II (MHC II)-specific activation of CD4+ T helper cells generates specific and persistent adaptive immunity against tumors. Emerging evidence demonstrates that MHC II is also involved in basic pain perception; however, little is known regarding its role in the development of cancer-induced bone pain (CIBP). In this study, we demonstrate that MHC II expression was markedly induced on the spinal microglia of CIBP rats in response to STAT1 phosphorylation. Mechanical allodynia was ameliorated by either pharmacological or genetic inhibition of MHC II upregulation, which was also attenuated by the inhibition of pSTAT1 and pERK but was deteriorated by intrathecal injection of IFNγ. Furthermore, inhibition of ERK signaling decreased the phosphorylation of STAT1, as well as the production of MHC II in vivo and in vitro. These findings suggest that STAT1 contributes to bone cancer pain as a downstream mediator of ERK signaling by regulating MHC II expression in spinal microglia