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The traditional blockchain deployment process is too complicated and has high technical requirements for blockchain deployers. Deploying a blockchain requires building a complex software-dependent environment, being able to use Linux commands for cumbersome parameter configurations, as well as the need to consider whether the hardware meets the requirements for running a blockchain. To address these current challenges in blockchain deployment both domestically and internationally, a web-based automatic deployment system with an interactive front-end and back-end has been developed. This system streamlines the process by automatically configuring and deploying blockchains while providing deployers with a graphical interface to monitor the entire deployment procedure. Meanwhile, in order to improve the efficiency of blockchain deployment, a Hidden Markov Model has been designed for blockchain deployment, which can predict the best deployment method for blockchain deployment under the current software environment. As one of the excellent blockchain platforms in China, Chainmaker has the outstanding features of independent control, flexible assembly, software and hardware integration, open source, and openness. The system takes Chainmaker as an experimental object and after a lot of tests, it can easily build a blockchain network on the server. [ABSTRACT FROM AUTHOR]

Whereas modern medicine has enabled humans to live longer and more robust lives, recent years have seen a significant increase in chronic care costs. The prevention of threats to mobility is critical for chronic disease management. Researchers and physicians often analyze data from wearable motion sensor–based information systems (IS) to prevent falls. However, prior studies on fall prevention often achieve suboptimal performance because of their limited capacities in modeling data distributions. In this study, we adopt the computational design science paradigm to develop a novel fall prevention framework, which includes the hidden Markov model with generative adversarial network (HMM-GAN) that extracts temporal and sequential patterns from sensor signals and recognizes snippet states and a logistic regression that utilizes the snippet states and determines whether and when to trigger protective devices to prevent fall injuries. We evaluate the proposed framework against prevailing fall-prevention models and the HMM-GAN component against state-of-the-art sensor analytics models on large-scale data sets. Through an in-depth case study, we demonstrate how the proposed framework can lead to significantly reduced potentially catastrophic falls. Besides practical health information technology contributions, HMM-GAN offers methodological contributions to the IS knowledge base for scholars designing novel IT artifacts for healthcare applications. Whereas modern medicine has enabled humans to live longer and more robust lives, recent years have seen a significant increase in chronic care costs. The prevention of threats to mobility, such as falls, freezing of gait, and others, is critical for chronic disease management. Researchers and physicians often analyze data from wearable motion sensor–based information systems (IS) to prevent falls because of their convenience, low cost, and user privacy protection. However, prior studies on fall prevention often achieve suboptimal performance because of their limited capacities in modeling data distributions. In this study, we adopt the computational design science paradigm to develop a novel fall prevention framework, which includes the hidden Markov model with generative adversarial network (HMM-GAN) that extracts temporal and sequential patterns from sensor signals and recognizes snippet states, and a logistic regression that utilizes the snippet states and determines whether and when to trigger protective devices to prevent fall injuries. Drawing upon the HMM, deep learning, and a new expectation-maximization instantiation, the proposed framework addresses limitations of existing methods by automatically extracting features from motion sensor data, accounting for both independent and sequential information in data snippets, operating on sensor signals with varying distributions and sharp peaks and valleys, allowing lead times, and being applicable in both semisupervised and supervised modes. We evaluate the proposed fall prevention framework against prevailing fall prevention models and the HMM-GAN component against state-of-the-art sensor analytics models on selected large-scale ground truth data sets containing thousands of falls and normal activities. Through an in-depth case study, we demonstrate how the proposed framework can lead to significantly reduced potentially catastrophic falls by senior citizens and produce more than $33 million of economic benefits over competing models. Besides practical health information technology contributions, HMM-GAN offers methodological contributions to the IS knowledge base for scholars designing novel information technology artifacts for healthcare applications. History: Olivia Sheng, Senior Editor; Huimin Zhao, Associate Editor. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72293581, 91846201, 72293580, 72188101, 72101079, 71771131, and 72110107003], the Division of Industrial Innovation and Partnerships [Grant 1622788], and the Division of Computer and Network Systems [Grant 1850362] of the National Science Foundation. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2023.1203. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, the electronifi- cation of medical records has gradually become a trend. In China, the population base is huge and the supporting medical institutions are numerous, so this reality drives the conversion of paper medical records to electronic medical records. Electronic medical records are the basis for establishing a smart hospital and an important guarantee for achieving medical intelligence, and the massive amount of electronic medical record data is also an important data set for conducting research in the medical field. However, electronic medical records contain a large amount of private patient information, which must be desensitized before they are used as open resources. Therefore, to solve the above problems, data masking for Chinese electronic medical records with named entity recognition is proposed in this paper. Firstly, the text is vectorized to satisfy the required format of the model input. Secondly, since the input sentences may have a long or short length and the relationship between sentences in context is not negligible. To this end, a neural network model for named entity recognition based on bidirectional long shortterm memory (BiLSTM) with conditional random fields (CRF) is constructed. Finally, the data masking operation is performed based on the named entity recognition results, mainly using regular expression filtering encryption and principal component analysis (PCA) word vector compression and replacement. In addition, comparison experiments with the hidden markov model (HMM) model, LSTM-CRF model, and BiLSTM model are conducted in this paper. The experimental results show that the method used in this paper achieves 92.72% Accuracy, 92.30% Recall, and 92.51% F1_score, which has higher accuracy compared with other models. [ABSTRACT FROM AUTHOR]

Multimodule and multisensor data with different characteristics can reflect the overall operating status of the equipment from different angles. It is far from enough to monitor the operating status of equipment from a single perspective for this fails to take all valid information into account. However, data integration may face problems such as the curse of dimensionality and scale mismatches. Therefore, decision fusion, which needs to measure and manage evidence conflicts, has attracted extensive attention from scholars. However, most of the state-of-art methods focus on conflict management based on evidence itself and ignore the irrationality of conflict factor $K$ in measuring conflicts and the reliability of evidence sources in conflict management. In order to solve the above problems, a novel hybrid decision fusion approach is proposed in this article. First, divide data into modules and use the models in the model library to conduct cross-validation, thus obtaining the performance ranking. Then, select the optimal classifier of each module to obtain the evidence for decision fusion. Given the conflict of evidences, the Jensen–Shannon (JS) divergence is used to measure the conflict, and those high-conflict evidences will be revised through sensitivity and support analyses. Finally, the Dempster–Shafer (DS) evidence theory is used to integrate multimodule evidences to assess the status. To prove the feasibility and effectiveness of this approach, a realistic operational shield case in China is used. [ABSTRACT FROM AUTHOR]

3-D printing is an additive manufacturing process, which enables products to be custom designed. Obtaining the evolution trend and identifying the potential R&D hotspots of 3-D printing technology are important strategic issues for both nations and enterprises. In this article, a combination method is proposed using the Viterbi algorithm to identify technological terms in patents, using the latent Dirichlet allocation model to capture potential technological topics in patents, and using the hidden Markov model to analyze the distribution and evolution pattern of the technological topics. Based on the results, technological topics and future R&D hotspots in 3-D printing are identified. Among the technological topics, extrusion devices and laser powder molding are gradually losing R&D; model acquisition, extrusion devices, rotary printing, and denture are the key topics of technological evolution that are difficult to overcome; laser focusing, print head, color printing, recyclable, feeding, control panel, and denture will become the focus of R&D in the near future. Finally, a comparison and experiments are given to demonstrate the plausibility of our method. [ABSTRACT FROM AUTHOR]

Although there is a growing number of research articles investigating the performance in the banking industry, research on Chinese banking efficiency is rather focused on discussing rankings to the detriment of unveiling its productive structure in light of banking competition. This issue is of utmost importance considering the relevant transformations in the Chinese economy over the last decades. This is a development of a two-stage network production process (production and intermediation approaches in banking, respectively) to evaluate the efficiency level of Chinese commercial banks. In the second stage regression analysis, an integrated Multi-Layer Perceptron/Hidden Markov model is used for the first time to unveil endogeneity among banking competition, contextual variables, and efficiency levels of the production and intermediation approaches in banking. The competitive condition in the Chinese banking industry is measured by Panar–Rosse H-statistic and Lerner index under the Ordinary Least Square regression. Findings reveal that productive efficiency appears to be positively impacted by competition and market power. Second, credit risk analysis in older local banks, which focus the province level, would possibly be the fact that jeopardizes the productive efficiency levels of the entire banking industry in China. Thirdly, it is found that a perfect banking competition structure at the province level and a reduced market power of local banks are drivers of a sound banking system. Finally, our findings suggest that concentration of credit in a few banks leads to an increase in bank productivity. [ABSTRACT FROM AUTHOR]

Rockburst is a kind of complex and catastrophic dynamic geological disaster in the development and utilization of underground space, which seriously threatens the safety of personnel and environment. Due to the suddenness in time and randomness in space, the prediction of rockburst becomes a great challenge. Microseismic monitoring is capable to continuously capture rock microfracture signals in real time, which offers an effective means for rockburst prediction. With the explosive growth of monitoring data, the conventional manual forecasting methods are laborious and time-consuming. Therefore, artificial intelligence was introduced to improve prediction efficiency. A novel tree-based algorithm was proposed. Its basic idea was to automatically recognize precursory microseismic sequences for the real-time prediction of rockburst intensity. The database consisting of 1500 microseismic events was analyzed. To establish precursory microseismic sequences, dimensionality reduction of the database was first implemented by t-SNE algorithm. Then, k-means clustering algorithm was employed for labelling 1500 microseismic events. Before that, canopy algorithm was adopted to determine the number of clusters. Finally, 300 precursory microseismic sequences were formed by the grouping rule. They were further partitioned into two parts through stratified sampling: 70% for training and 30% for validation. The validation results indicated that the precursor tree with pruning achieved a higher prediction accuracy of 98.9% than one without pruning on the validation set. And the increase was separately 12.2%, 9.2% and 28.6% on the whole validation set and each classes (low/moderate rockburst). In comparison with low rockburst, moderate rockburst was minority class. The improved accuracy on moderate rockburst suggested that pruning can enhance the recognition ability of precursor tree for the minority class. Additionally, two extra rockburst cases were collected from a diversion tunnel in northwestern China, which provided a complete workflow about how to apply the built precursor tree model to achieve field rockburst warning in engineering practice. The tree-based algorithm served as a new and promising way for the real-time rockburst prediction, which successfully integrated field microseismic monitoring and artificial intelligence. [ABSTRACT FROM AUTHOR]

With the growth in the living standard of people, food safety and quality risk assessment has gradually become the focus. Therefore, this article realizes the dynamic risk assessment of food safety based on an improved hidden Markov model (HMM) integrating the cuckoo search algorithm (CS) (CS‐HMM). The CS is used to conduct global search to obtain the initial value of HMM, and then the Baum–Welch algorithm is used for modifying the initial value to obtain a trained risk assessment model. Finally, in terms of several benchmark functions, the accuracy of the CS algorithm is better than the PSO and the GA algorithms for seeking global optimal solution. Moreover, the presented method is applied to evaluate the safety risk of the sterilized milk production. Combined with the characteristics of time and temperature, the deterioration reasons of sterilized milk are analyzed. Moreover, the proposed method has certain theoretical significance and practical value for improving the ability of food quality risk assessment and formulating control measures in advance. Practical applications: The presented method is applied to evaluate the safety risk of the sterilized milk production, which are supplied by the food inspection agency of Guizhou province in China. Combined with the characteristics of time and temperature, the deterioration reasons of sterilized milk are analyzed. Furthermore, the improved model breaks out of the local optimal limit, so as to better realize the risk prediction of food and obtain better evaluation results. Based on the analysis of dynamic risk assessment result with the temperature characteristics, the future risk trend can be predicted. Meanwhile, the causes affecting the deterioration of the sterilized milk and the direction to improve the quality of the sterilized milk can be obtained. In this way, risk control measures can be taken more precisely and risks can be reduced more effectively, which is helpful to the production and storage of food products. [ABSTRACT FROM AUTHOR]

With the emergence of the Internet of Things (IoT), there has been a proliferation of urban studies using big data. Yet, another type of urban research innovations that involve interdisciplinary thinking and methods remains underdeveloped. This paper represents an attempt to adopt a Hidden Markov Model (HMM) toolbox developed in Computer Science for the analysis of eye movement patterns in Psychology to answer urban mobility questions in Geography. The main idea is that both people's eye movements and travel behavior follow the stop-travel-stop pattern, which can be summarized using HMM. Methodological challenges were addressed by adjusting the HMM to analyze territory-wide travel survey data in Hong Kong, China. By using the adjusted toolbox to identify the activity-travel patterns of working adults in Hong Kong, two distinctive groups of balanced (38.4%) and work-oriented (61.6%) lifestyles were identified. With some notable exceptions, working adults living in the urban core were having a more work-oriented lifestyle. Those with a balanced lifestyle were having a relatively compact zone of non-work activities around their homes but a relatively long commuting distance. Furthermore, working females tend to spend more time at home than their counterparts, regardless of their marital status and lifestyle. Overall, this interdisciplinary research demonstrates an attempt to integrate spatial, temporal, and sequential information for understanding people's behavior in urban mobility research. [ABSTRACT FROM AUTHOR]

The spatial distribution of mineralization includes significant clues about the magma/fluid flow process for ore formation. However, given the complex nature of the ore-forming process and its weak signature in the mineralization data, revealing the magma/fluid flow process in their mineralization footprint presents a considerable challenge. In this paper, we present a hidden Markov model (HMM) for the analysis of three-dimensional (3D) spatial distribution of mineralization. The HMM assumes the magma flow is a Markov process that is "hidden" in the "observable" mineralization features. Under this setting, the HMM quantifies the states of magma flow and links them as a whole in a probabilistic manner. This allows for the comprehensive combination of mineralization information, facilitating holistic inference of magma flow hidden in the mineralization signal. Based on the maximum likelihood principle, the magma trajectories and the associated injection points are inferred from the HMM. The proposed model is applied to spatial analysis of the mineralization in the world-class Jinchuan Ni-Cu-(PGE) magmatic sulfide deposit in NW China. Based on the 3D mineralization data interpolated from drill assays, the HMM infers magma trajectories and the associated injection points at Jinchuan, which indicate the Jinchuan deposit was formed by a multiple magma conduit system that comprises four independent injection points of magma ingress. According to the identified magma trajectories and injection points, four sites beneath the injection points and along the extension of magma trajectories are considered promising for future mineral exploration. With the insights gained from this case study, the proposed HMM can serve as an effective tool in the exploration information system to unveil magma/fluid flow history and guide exploration efforts toward concealed, deep-seated mineralization. • A Hidden Markov model for spatial analysis of three-dimensional mineralization distribution. • A spatial analysis approach to gain insights into magma flow in their mineralization footprint. • Magma trajectories are inferred by maximizing their posterior probability. • A promising tool for converting data to insight in exploration information system. • Jinchuan Ni–Cu-(PGE) deposit was possibly formed by a multiple magma conduit subsystem. [ABSTRACT FROM AUTHOR]

BACKGROUND: Due to frequent accidents and physical diseases, a large number of people have lost the ability to walk. How to gradually restore the walking ability of these patients through motion recognition of the human body with an exoskeleton walker has gradually become a hot topic in the field of medical rehabilitation engineering. OBJECTIVE: To improve the recognition rate of human gait by fusion of surface electromyography signals and triaxial acceleration signals at classifier level. METHODS: An approach based on combining surface electromyography signals and triaxial acceleration signals was proposed to recognize five different kinds of basis daily gait patterns, including walking on the ground, going up stairs, going down stairs, going up slope and going down slope. The surface electromyography signals of five channels in the lower limbs and triaxial acceleration signals at the thighs and calves were collected. After signal pre-processing, the features of fusion signals were extracted. A classifier based on two-stream Hidden Markov Model was constructed to train the classifier. The classifier was trained and tested with test set to obtain the recognition accuracy. RESULTS AND CONCLUSION:(1) Five basic gait patterns were identified. The experiment obtained an average recognition accuracy of 94.32%, which was 4.15% higher than the accuracy by adopting surface electromyography signal only(average 90.17%), and 9.60% higher than the accuracy by adopting acceleration signal only(average 84.72%).(2) The results showed that more useful movement information could be obtained by combining surface electromyography signal and acceleration signal, which can help improve the recognition accuracy. [ABSTRACT FROM AUTHOR]

Traditional stock market prediction methods commonly only utilize the historical trading data, ignoring the fact that stock market fluctuations can be impacted by various other information sources such as stock-related events. Although some recent works propose event-driven prediction approaches by considering the event data, how to leverage the joint impacts of multiple data sources still remains an open research problem. In this work, we study how to explore multiple data sources to improve the performance of the stock prediction. We introduce an extended coupled hidden Markov model incorporating the news events with the historical trading data. To address the data sparsity issue of news events for each single stock, we further study the fluctuation correlations between the stocks and incorporate the correlations into the model to facilitate the prediction task. Evaluations on China A-share market data in 2016 show the superior performance of our model against previous methods. [ABSTRACT FROM AUTHOR]

In recent years, bike-sharing systems have been widely deployed in many big cities, which provide an economical and healthy lifestyle. With the prevalence of bike-sharing systems, a lot of companies join the bike-sharing market, leading to increasingly fierce competition. To be competitive, bike-sharing companies and app developers need to make strategic decisions and predict the popularity of bike-sharing apps. However, existing works mostly focus on predicting the popularity of a single app, the popularity contest among different apps has not been explored yet. In this paper, we aim to forecast the popularity contest between Mobike and Ofo, two most popular bike-sharing apps in China. We develop CompetitiveBike, a system to predict the popularity contest among bike-sharing apps leveraging multi-source data. We extract two novel types of features: coarse-grained and fine-grained competitive features, and utilize Random Forest model to forecast the future competitiveness. In addition, we view mobile apps competition as a long-term event and generate the event storyline to enrich our competitive analysis. We collect data about two bike-sharing apps and two food ordering & delivery apps from 11 app stores and Sina Weibo, implement extensive experimental studies, and the results demonstrate the effectiveness and generality of our approach. [ABSTRACT FROM AUTHOR]

Monthly precipitation modeling is important in various applications, e.g. streamflow forecasts and water resources management. This paper develops an operational precipitation forecasting scheme, using Bayesian Non-homogeneous Hidden Markov Chain (NHMM) model and teleconnection index. Although the Hidden Markov Chain model has been investigated before in similar studies, the NHMM algorithm employed in this study allows modeling both non-stationary transition probabilities and emission matrix. Climatic teleconnection that affect precipitation is used to drive changes in transition probabilities of different states in the Markov model. The proposed framework is illustrated for multiple-station precipitation analysis in NingXiang County, a southern inland area in China with a high population density. A simulation model is constructed to examine the model's capacity in capturing variabilities and temporal-spatial characteristics exhibiting in monthly precipitation data during 1961-2013. Results indicate that the proposed NHMM model captures the precipitation characteristics at different stations well. Spearman correlation between conditional mean of simulated ensembles and observed data is 0.87-0.9, with few variations at distinct stations. The proposed framework has general applications and can be applied to simulate and generate stochastic monthly precipitation. Further application of the method is also described in the paper. [ABSTRACT FROM AUTHOR]

A nonhomogeneous hidden Markov model (NHMM) is used to stochastically simulate summer (June–August) daily precipitations in the middle and low reaches of the Yangtze River in Eastern China, with driving forcing from three global climate models (GCMs). Simulations cover the historical period from 1961 to 2005 and from 2006 to 2100 following the RCP4.5 scenario. The model is first evaluated against data from the regional observation network. The results show that NHMM effectively enhances the ability of GCMs in simulating summer daily rainfall in the region. For future projection at different time horizons of the 21st century, the spectral distribution of regional precipitations (in function of their intensity) shows consistent changes with a decrease of occurrence probability for light rain (<10 mm/day) and an increase for heavy rain (>10 mm/day). Among variables of interest, total precipitation (PRCPTOT), precipitation intensity, the number of rainy days for daily precipitation exceeding 10 mm (R10mm) and 95th percentile of precipitation (P95), all show a gradually increasing trend in the 21st century, and geographically an eastward gradient with smaller increase (or even weak decrease) for the west and larger increase for the east. It is noted that obvious changes occur in the eastern region with 95% significance level, and PRCPTOT or R10mm increases by 40–60% in the late 21st century. Further quantitative assessment is performed for global warming of 1.5 and 2°C. The half‐degree additional warming makes R10mm change by −3.7, 2.4 and 12.1% over western, central and eastern regions, respectively. Figure Boxplots showing the distribution of relative changes (in %) of R10mm (number of rainy days for daily precipitation more than 10mm, a), P95 (95th percentile of precipitation, b) and CDD (maximum consecutive dry days, c) in the three sub‐regions, western (light grey, 110–114°E), central (dark grey, 114–118°E) and eastern (black, 118–122°E), under the global warming of 1.5, 2°C and half‐degree warming from 1.5 to 2°C. The upper and lower limits of box indicate the 75th and 25th percentiles among stations; the horizontal line inside box indicates the 50th percentiles among stations; the asterisk inside box represents multi‐station ensemble median (mean); and the whiskers show the range among stations. [ABSTRACT FROM AUTHOR]

Due to the multifaceted influences of many factors and complex mechanisms, accurate predictions of ocean chemistry data are difficult to achieve. To address this problem, this paper proposes an adaptive hidden Markov model (AHMM) for predicting ocean chemistry data from aquaculture farms in eastern China. The periodic, trend and residual terms of the original sequence are obtained through Seasonal and Trend decomposition using Loess (STL), which effectively reveals the variation trend of the sequence. For the period and trend terms of the sequence, the autonomous search grasshopper optimization algorithm (ASGOA) is employed to train the parameters of the HMM, which avoids the problems of easily falling into local optima and the dependence on the initial value in the parameter training process. To predict ocean chemistry data, a sequence similarity measurement method is proposed to match similar sequences in the historical interval, and a linear regression equation is constructed for sequence similarity and the difference between the predicted value and the value at the previous time based on the HMM. The results have revealed that the MAE value decreases by 25.5%, MSE value by 45.9%, RMSE value by 27.4%, MAPE value by 39.5% and R 2 increases by 6%. Compared with other prediction algorithms under the same conditions, the prediction method proposed in this paper is easily understandable and manages to achieve better prediction performance. This work can serve as a reference for predicting other ocean chemistry data and for the sustainable management of aquaculture ecosystems. • A water quality prediction method based on adaptive hidden Markov model is proposed. • An automatic search grasshopper optimization algorithm (ASGOA) is proposed. • A similarity measurement method for ocean chemistry data prediction is proposed. • AHMM has better prediction performance. [ABSTRACT FROM AUTHOR]

The proposed policy targets of "2030 carbon peaking" and "2060 carbon neutrality" further accelerate the substitution of natural gas for oil and coal. In this paper, we systematically considered the growth trend of natural gas demand from both the total amount and the structure of energy.On the basis of 60-year sample data, we built the HMM model innovatively to simulate the complementary and alternative structural effects between different energy. In addition, according to the different policy intensity, we simulated the history of energy demand and forecast the future trend in three scenarios. The empirical results show that the share of natural gas in primary energy consumption in China in 2035 is 16.5%, 17.3%, and 13.6%, respectively, translating into natural gas demand of 6975, 7316, and 5748 bcm. Therefore, policymakers should promote energy transformation in a steady and orderly manner based on national conditions and strengthen gas demand-side management on the premise of ensuring reasonable demand. Our prediction of natural gas demand from a new perspective can assist policymakers to formulate the natural gas demand-side policy and import plan, and also provide a reference for studies of achieving the goal of energy transition and "2060 carbon neutrality". • The overall and structural effects of gas demand growth. • The evolution trend of energy demand structure is predicted through three ideas. • Structure model is used to simulate the substitution of energy sources. • The prediction results of hidden Markov model are more reasonable. [ABSTRACT FROM AUTHOR]

Microblogging is increasingly becoming one of the most popular online social media for people to express ideas and emotions. The amount of socially generated content from this medium is enormous. Text mining techniques have been intensively applied to discover the hidden knowledge and emotions from this huge dataset. In this paper, we propose a modified version of hidden Markov model (HMM) classifier, called self-adaptive HMM, whose parameters are optimized by Particle Swarm Optimization algorithms. Since manually labeling large-scale dataset is difficult, we also employ the entropy to decide whether a new unlabeled tweet shall be contained in the training dataset after being assigned an emotion using our HMM-based approach. In the experiment, we collected about 200,000 Chinese tweets from Sina Weibo. The results show that the F-score of our approach gets 76% on happiness and fear and 65% on anger, surprise, and sadness. In addition, the self-adaptive HMM classifier outperforms Naive Bayes and Support Vector Machine on recognition of happiness, anger, and sadness. [ABSTRACT FROM AUTHOR]

• This study developed a stepwise NHMM-VAR multisite downscaling model. • The NHMM-VAR model performs well not only in modeling the statistical properties but also in reproducing the spatiotemporal correlations of multisite precipitation amounts. • The temporal intermittency of precipitation amounts is considered in the NHMM-VAR model. The nonhomogeneous hidden Markov model (NHMM) is a popular statistical downscaling (SD) approach that can be used for multisite precipitation estimation. However, the NHMM assumes that once the days with the occurrence of precipitation have been determined, the precipitation amounts at each station on wet days can be conditionally independently generated from a parametric family of probability distribution functions without considering impacts from the past precipitation amounts and the neighboring stations. Such assumptions may lead to underestimation of the spatiotemporal correlations (i.e., temporal autocorrelation and spatial cross-correlations) of actual multisite precipitation amounts. Thus, this study developed a stepwise downscaling method called the nonhomogeneous hidden Markov model - vector autoregressive (NHMM-VAR), in which the NHMM is used to determine the probabilities for dry days and marginal distributions of multisite daily mean precipitation amounts on wet days based on the large-scale atmospheric covariates, then a VAR(1) model is used to reproduce the spatiotemporal correlations of actual multisite daily mean precipitation amounts. A comparison experiment between the NHMM and the NHMM-VAR models is conducted against outputs from eight global climate models (GCMs) of Phase 6 of the Coupled Model Intercomparison Project (CMIP6) over the Pearl River basin (PRB) of South China. The results show that the NHMM-VAR model performs well not only in modeling the statistical properties but also in reproducing the spatiotemporal correlations of actual multisite daily mean precipitation amounts, which indicates that the VAR model does add value to the NHMM downscaling method. [ABSTRACT FROM AUTHOR]

A novel sensor fusion algorithm for retrieving the freeze/thaw (f/t) state from scatterometer data is presented: It is based on a probabilistic model, which is a variant of the Hidden Markov model, and it computes the probability that the landscape is frozen, thawed, or thawing for each day. By combining Ku- and C-band scatterometer data, the distinct backscattering properties of snow, soil, and vegetation at the two radar bands are exploited. The parameters that are necessary for inferring the f/t state are estimated in an unsupervised fashion, i.e., no training data are required. Comparison with model and in situ temperature data in a test area in Siberia/northern China indicates that the approach yields promising results (typical accuracies exceeding 90%); difficulties are encountered over bare rock and areas where large fluctuations in soil moisture are common. These limitations turn out to be closely linked to the inherent assumptions of the probabilistic model. [ABSTRACT FROM AUTHOR]

The unobservable state of stock information was modeled based on Hidden Markov Model, and a transition probabilities matrix of information state was built to describe the dynamic association properties in temporal dimension. Based on 5-minutes high frequency trading data and using Bayesian inference and MCMC sampling, the information state and strength of Shanghai Stock Index and sample stocks of SSE 50 in August 2010 were estimated. Empirical results prove the model has effective ability to identify information, and show that the information effects have characteristics of aggregation in Chinese stock market. On the base of the estimated transition probabilities matrix of information state, it is surmised that the probability that an item of information was absorbed by the market after 100 minutes is 99% in Chinese stock market. [ABSTRACT FROM AUTHOR]

Two statistical downscaling (SD) models, the nonhomogeneous hidden Markov model (NHMM) and the statistical down-scaling model (SDSM), which have been widely applied and proved skillful in terms of downscaling precipitation, were evaluated based on observed daily precipitation over the Tarim River basin, an arid basin located in China. The evaluated metrics included residual functions, correlation analyses, probability density functions (PDFs) and distributions. Overall, both models exhibited stability with little model performance difference between the calibration and validation periods. There was little difference for model performance on dry-spell length (dsl) and wet-spell length (wsl) between NHMM and SDSM. NHMM showed skill in simulating wet-day precipitation amount (wpa), whilst SDSM performed relatively poorly on extreme values of wpa, especially for dry stations with annual precipitation lower than 200 mm. Both NHMM and SDSM captured the spatial distribution characteristics of precipitation for most of the stations, as 11 months had an at-station correlation coefficient being greater than 0.9 and 0.8 for NHMM and SDSM in calibration period. NHMM showed slightly better model performance than SDSM on simulating monthly precipitation, as the former was able to model precipitation well for all months, whereas the later was well only for certain months. SDSM was able to capture the inter-site correlation characteristics of observed series, whilst the NHMM multi-site simulation over estimated inter-site correlation. Both NHMM and SDSM had less skill downscaling annual series because of stochastic components in precipitation amounts modelling. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

With the development of modern intelligent transportation systems (ITS), automatic traffic incident detection with quick response and high accuracy becomes one of the most important issues, especially for metropolitan streets that are full of signaled intersections. In this paper, we present our up-to-date research outcomes of the traffic incident detection system, which makes use of the image sequences gathered from a typical urban intersection. Basic image signal processing was used to extract image difference information for traffic image database construction. Feature extraction algorithms were then discussed and compared including PCA, FFT, and hybrid analysis of DCT-FFT. Finally, multi-classification of traffic signal logics (East-West, West-East, South-North, North-South) and accidents were realized by HMM (Hidden Markov Model) and SVM (Support Vector Machine) respectively. Experimental results showed that the hybrid DCT-FFT method gives the best features, and classification performance of SVM is superior to HMM with limited training samples, where the correction rate is 100% for SVM and 91% for HMM. [ABSTRACT FROM AUTHOR]

Simple Summary: Grass carp, Ctenopharyngodon idellus, is an important freshwater cultured teleost in China, and its annual production has reached 5,533,083 tons. However, its aquaculture is severely restricted by hemorrhagic disease caused by the grass carp reovirus (GCRV). For the better control of grass carp hemorrhagic disease, the breeding of resistant grass carp strains based on antiviral immune molecule markers is a potential solution. However, the molecular basis of grass carp's resistance to GCRV infection remains largely unknown, greatly limiting the breeding of grass carp resistant to hemorrhagic disease. Given the importance of tripartite motif proteins (TRIMs) in animal antiviral immunity, we used the Hidden Markov Model Biological Sequence Analysis software (HMMER) and SMART to identify TRIMs in the grass carp genome and analyze their gene loci, as well as structural and evolutionary features. We also tried to uncover antiviral TRIMs and their mediated immune processes based on two sets of transcriptomes during GCRV infection in grass carp. This study provides information for the understanding of TRIMs and antiviral immunity in grass carp. Tripartite motif proteins (TRIMs), especially B30.2 domain-containing TRIMs (TRIMs-B30.2), are increasingly well known for their antiviral immune functions in mammals, while antiviral TRIMs are far from being identified in teleosts. In the present study, we identified a total of 42 CiTRIMs from the genome of grass carp, Ctenopharyngodon idella, an important cultured teleost in China, based on hmmsearch and SMART analysis. Among these CiTRIMs, the gene loci of 37 CiTRIMs were located on different chromosomes and shared gene collinearities with homologous counterparts from human and zebrafish genomes. They possessed intact conserved RBCC or RB domain assemblies at their N-termini and eight different domains, including the B30.2 domain, at their C-termini. A total of 19 TRIMs-B30.2 were identified, and most of them were clustered into a large branch of CiTRIMs in the dendrogram. Tissue expression analysis showed that 42 CiTRIMs were universally expressed in various grass carp tissues. A total of 11 significantly differentially expressed CiTRIMs were found in two sets of grass carp transcriptomes during grass carp reovirus (GCRV) infection. Three of them, including Cibtr40, CiTRIM103 and CiTRIM109, which all belonged to TRIMs-B30.2, were associated with the type I interferon response during GCRV infection by weighted network co-expression and gene expression trend analyses, suggesting their involvement in antiviral immunity. These findings may offer useful information for understanding the structure, evolution, and function of TRIMs in teleosts and provide potential antiviral immune molecule markers for grass carp. [ABSTRACT FROM AUTHOR]

Advances in speech-processing technology have enabled novel ways to learn a foreign language online. With Engkoo, researchers in China are working to turn any computer into a language learning assistant and make searching a language easier. The featured Web extra at http://youtu.be/_VHDMAKKLKo is a video discussion titled “Computer-Assisted Audiovisual Language Learning” that demonstrates Engkoo, a Web-based computer-assisted audiovisual language-learning service that combines two emerging speech processing technologies—talking head and phonetic similarity search. [ABSTRACT FROM PUBLISHER]