A single neuron PID control method based on Smith predictor for active balancing control of rotor with time-delay

Time-delay is a key problem affecting the performance of active balancing control. In this paper, a single neuron PID control method based on Smith predictor for active balancing control of rotor with time-delay is proposed. The dynamic model of rotor is built by simplification firstly in the paper. Based on this dynamic model, a compensation model of time-delay rotor system is constructed, and a single neuron PID control method is designed to reduce rotor vibration. The parameters of controller can be adjusted adaptively and active balancing control of rotor with time-delay is realized efficiently. The simulation shows that the proposed method is superior to the traditional Smith predictor method in control performance. Particularly the proposed method still has good effect on control of rotor with large time-delay

An Integrated Framework Based on Latent Variational Autoencoder for Providing Early Warning of At-Risk Students

The rapid development of learning technologies has enabled online learning paradigm to gain great popularity in both high education and K-12, which makes the prediction of student performance become one of the most popular research topics in education. However, the traditional prediction algorithms are originally designed for balanced dataset, while the educational dataset typically belongs to highly imbalanced dataset, which makes it more difficult to accurately identify the at-risk students. In order to solve this dilemma, this study proposes an integrated framework (LVAEPre) based on latent variational autoencoder (LVAE) with deep neural network (DNN) to alleviate the imbalanced distribution of educational dataset and further to provide early warning of at-risk students. Specifically, with the characteristics of educational data in mind, LVAE mainly aims to learn latent distribution of at-risk students and to generate at-risk samples for the purpose of obtaining a balanced dataset. DNN is to perform final performance prediction. Extensive experiments based on the collected K-12 dataset show that LVAEPre can effectively handle the imbalanced education dataset and provide much better and more stable prediction results than baseline methods in terms of accuracy and F1.5 score. The comparison of t-SNE visualization results further confirms the advantage of LVAE in dealing with imbalanced issue in educational dataset. Finally, through the identification of the significant predictors of LVAEPre in the experimental dataset, some suggestions for designing pedagogical interventions are put forward

Self-organization and phase transition in financial markets with multiple choices

Market confidence is essential for successful investing. By incorporating multi-market into the evolutionary minority game, we investigate the effects of investor beliefs on the evolution of collective behaviors and asset prices. When there exists another investment opportunity, market confidence, including overconfidence and under-confidence, is not always good or bad for investment. The roles of market confidence is closely related to market impact. For low market impact, overconfidence in a particular asset makes an investor become insensitive to losses and a delayed strategy adjustment leads to a decline in wealth, and thereafter, one's runaway from the market. For high market impact, under-confidence in a particular asset makes an investor over-sensitive to losses and one's too frequent strategy adjustment leads to a large fluctuation in asset prices, and thereafter, a decrease in the number of agents. At an intermediate market impact, the phase transition occurs. No matter what the market impact is, an equilibrium between different markets exists, which is reflected in the occurrence of similar price fluctuations in different markets. A theoretical analysis indicates that such an equilibrium results from the coupled effects of strategy updating and shift in investment. The runaway of the agents trading a specific asset will lead to a decline in the asset price volatility and such a decline will be inhibited by the clustering of the strategies. A uniform strategy distribution will lead to a large fluctuation in asset prices and such a fluctuation will be suppressed by the decrease in the number of agents in the market. A functional relationship between the price fluctuations and the numbers of agents is found

\u3ci\u3eIn-silico\u3c/i\u3e prediction of blood-secretory human proteins using a ranking algorithm

Background: Computational identification of blood-secretory proteins, especially proteins with differentially expressed genes in diseased tissues, can provide highly useful information in linking transcriptomic data to proteomic studies for targeted disease biomarker discovery in serum. Results: A new algorithm for prediction of blood-secretory proteins is presented using an information-retrieval technique, called manifold ranking. On a dataset containing 305 known blood-secretory human proteins and a large number of other proteins that are either not blood-secretory or unknown, the new method performs better than the previous published method, measured in terms of the area under the recall-precision curve (AUC). A key advantage of the presented method is that it does not explicitly require a negative training set, which could often be noisy or difficult to derive for most biological problems, hence making our method more applicable than classification-based data mining methods in general biological studies. Conclusion: We believe that our program will prove to be very useful to biomedical researchers who are interested in finding serum markers, especially when they have candidate proteins derived through transcriptomic or proteomic analyses of diseased tissues. A computer program is developed for prediction of blood-secretory proteins based on manifold ranking, which is accessible at our website http://csbl.bmb.uga.edu/publications/materials/qiliu/ blood_secretory_protein.html

Tetra­kis(μ-4-tert-butyl­benzoato)-κ4 O:O′;κ3 O,O′:O′;κ3 O:O,O′-bis­[aqua­(4-tert-butyl­benzoato-κ2 O,O′)(4-tert-butyl­benzoic acid-κO)neodymium(III)]

The reaction of neodymium nitrate and 4-tert-butyl­benzoic acid (tBBAH) in aqueous solution yielded the dinuclear title complex, [Nd2(C11H13O2)6(C11H14O2)2(H2O)2], which has non-crystallographic Ci symmetry. The two NdIII ions are linked by two bridging and two bridging–chelating tBBA ligands with an Nd⋯Nd separation of 4.0624 (5) Å. Moreover, each NdIII ion is coordinated by one chelating tBBA ion, one monodentate tBBAH ligand and one water mol­ecule. The nine-coordinated NdIII ion is in a distorted tricapped trigonal–prismatic environment. The mol­ecules are linked into infinite chains along the c axis by inter­molecular O—H⋯O hydrogen bonds. Three of the tert-butyl groups are disordered over two sets of sites with equal occupancies

In-silico prediction of blood-secretory human proteins using a ranking algorithm

<p>Abstract</p> <p>Background</p> <p>Computational identification of blood-secretory proteins, especially proteins with differentially expressed genes in diseased tissues, can provide highly useful information in linking transcriptomic data to proteomic studies for targeted disease biomarker discovery in serum.</p> <p>Results</p> <p>A new algorithm for prediction of blood-secretory proteins is presented using an information-retrieval technique, called <it>manifold ranking</it>. On a dataset containing 305 known blood-secretory human proteins and a large number of other proteins that are either not blood-secretory or unknown, the new method performs better than the previous published method, measured in terms of the area under the recall-precision curve (AUC). A key advantage of the presented method is that it does not explicitly require a negative training set, which could often be noisy or difficult to derive for most biological problems, hence making our method more applicable than classification-based data mining methods in general biological studies.</p> <p>Conclusion</p> <p>We believe that our program will prove to be very useful to biomedical researchers who are interested in finding serum markers, especially when they have candidate proteins derived through transcriptomic or proteomic analyses of diseased tissues. A computer program is developed for prediction of blood-secretory proteins based on manifold ranking, which is accessible at our website <url>http://csbl.bmb.uga.edu/publications/materials/qiliu/blood_secretory_protein.html</url>.</p