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Abstract Background Clinical studies often track dose-response curves of subjects over time. One can easily model the dose-response curve at each time point with Hill equation, but such a model fails to capture the temporal evolution of the curves. On the other hand, one can use Gompertz equation to model the temporal behaviors at each dose without capturing the evolution of time curves across dosage. Results In this article, we propose a parametric model for dose-time responses that follows Gompertz law in time and Hill equation across dose approximately. We derive a recursion relation for dose-response curves over time capturing the temporal evolution and then specify a regression model connecting the parameters controlling the dose-time responses with individual level proteomic data. The resultant joint model allows us to predict the dose-response curves over time for new individuals. Conclusion We have compared the efficacy of our proposed Recursive Hybrid model with individual dose-response predictive models at desired time points. We note that our proposed model exhibits a superior performance compared to the individual ones for both synthetic data and actual pharmacological data. For the desired dose-time varying genetic characterization and drug response values, we have used the HMS-LINCS database and demonstrated the effectiveness of our model for all available anticancer compounds.

• The drift of E-nose will destroy the initially learned capability of pattern recognition after a certain period of time. • Due to drift is the inherent behavior of E-nose, the drift correction based on no-load condition has more practical value. • A drift correction method based on wavelet packet decomposition and no-load data was proposed in the manuscript. • Based on the concept of SMTW and the idea of recursive model, a robust model was availably constructed. • We think that the research results can lay some foundation for improving the long-term robust detection ability of E-nose. Due to sensor aging, environmental changes and other factors, the drift of electronic nose (E-nose) signal is inevitable, and make E-nose does not own long-term robust detection capability. In order to improve the long-term detection capability of E-nose, a drift correction method based on wavelet packet decomposition and no-load data acquisition is proposed. Firstly, a no-load threshold function (NLTF) was proposed by decomposing the no-load data of the E-nose using wavelet packet decomposition, and then the NLTF was converted into a threshold function suiting for sample data (i.e. sample threshold function, STF). Secondly, Based on a concept of "sample measurement time window" (SMTW), the STF was employed to process the sample data within the SMTW; so that the drift contained in the sample data could be corrected. Finally, when the SMTW was recursively moved forward, the drift in all sample data corresponding to different time (or SMTW) could be corrected. As a study case, to realize the long-term robust detection of 6 kinds of Chinese spirits, the six kinds of Chinese spirits samples were tested intermittently for 12 months. When the SMTW was 3 months and the SMTW moved recursively forward 1 month every time, and after the above-mentioned drift correction method was applied to deal with these samples data within the SMTW, a long-term robust detection model based on Fisher discriminant analysis (FDA) was constructed with help of the idea of recursive correction. The model was able to carry out long-term robust detection for the six spirits samples; the correct identification rate could reach 100%. In addition, we also believe that the drift correction method has certain reference value for other E-nose data. [ABSTRACT FROM AUTHOR]

Background: Clinical studies often track dose-response curves of subjects over time. One can easily model the dose-response curve at each time point with Hill equation, but such a model fails to capture the temporal evolution of the curves. On the other hand, one can use Gompertz equation to model the temporal behaviors at each dose without capturing the evolution of time curves across dosage. Results: In this article, we propose a parametric model for dose-time responses that follows Gompertz law in time and Hill equation across dose approximately. We derive a recursion relation for dose-response curves over time capturing the temporal evolution and then specify a regression model connecting the parameters controlling the dose-time responses with individual level proteomic data. The resultant joint model allows us to predict the dose-response curves over time for new individuals. Conclusion: We have compared the efficacy of our proposed Recursive Hybrid model with individual dose-response predictive models at desired time points. We note that our proposed model exhibits a superior performance compared to the individual ones for both synthetic data and actual pharmacological data. For the desired dose-time varying genetic characterization and drug response values, we have used the HMS-LINCS database and demonstrated the effectiveness of our model for all available anticancer compounds. [ABSTRACT FROM AUTHOR]

This paper presents a bio-economic optimization modeling approach for the simulation of land use decision making by farmers faced with climatic uncertainties. The approach is applied to the study of land use strategies on commercial ranches in Namibia. First, we compare two models differing in their structure: the first one is an inter-temporal optimization model (forward-looking with perfect foresight) while the second is recursive and it explicitly incorporates uncertainty in the decision making process. Second, we point out the structural advantage of the recursive optimization model in its ability to simulate how decision makers'' perceptions on the occurrence of stochastic events alter land use strategies and their economic and ecological outcomes. Both models make use of a State-and-Transition conceptual framework to depict the bio-economic feedback. We found that the incorporation of rainfall uncertainty in decision making is crucial when modeling land use strategies in highly variable ecological–economic systems such as ranches in arid rain-fed areas. Where knowledge of rainfall distribution is inaccurate (due to lack of experience or climate change) both, farmers and rangelands, would be better off by precautiously expecting low rainfalls. Finally, our results show that minimizing herd size adjustment costs would support the establishment of sustainable land use strategies. [Copyright &y& Elsevier]

Most methods for foreground region detection in videos are challenged by the presence of quasi-stationary backgrounds—flickering monitors, waving tree branches, moving water surfaces or rain. Additional difficulties are caused by camera shake or by the presence of moving objects in every image. The contribution of this paper is to propose a scene-independent and non-parametric modeling technique which covers most of the above scenarios. First, an adaptive statistical method, called adaptive kernel density estimation (AKDE), is proposed as a base-line system that addresses the scene dependence issue. After investigating its performance we introduce a novel general statistical technique, called recursive modeling (RM). The RM overcomes the weaknesses of the AKDE in modeling slow changes in the background. The performance of the RM is evaluated asymptotically and compared with the base-line system (AKDE). A wide range of quantitative and qualitative experiments is performed to compare the proposed RM with the base-line system and existing algorithms. Finally, a comparison of various background modeling systems is presented as well as a discussion on the suitability of each technique for different scenarios. [ABSTRACT FROM AUTHOR]

Inspired by the linear predictability and nonlinearity found in the finance literature, this article examines the nonlinear predictability of the excess returns. The relationship between the excess returns and the predicting variables is recursively modeled by a neural-network model, which is capable of performing flexible nonlinear functional approximation. The nonlinear neural-network model is found to have better in-sample fit and out-of-sample forecasts compared to its linear counterpart. Moreover, the switching portfolio based on the recursive neural-network forecasts generates higher profits with lower risks than both the buy-and-hold market portfolio and the switching portfolio based on linear recursive forecasts. [ABSTRACT FROM AUTHOR]

Individuals exhibit theory of mind, attributing beliefs, intent, and mental states to others as explanations of observed actions. Dennett's intentional stance offers an analogous abstraction for computational agents seeking to understand, explain, or predict others' behaviors. These recognized theories provide a formal basis to ongoing investigations of recursive modeling. We review and situate various frameworks for recursive modeling that have been studied in game- and decision- theories, and have yielded methods useful to AI researchers. Sustained attention given to these frameworks has produced new analyses and methods with an aim toward making recursive modeling practicable. Indeed, we also review some emerging uses and the insights these yielded, which are indicative of pragmatic progress in this area. The significance of these frameworks is that higher-order reasoning is critical to correctly recognizing others' intent or outthinking opponents. Such reasoning has been utilized in academic, business, military, security, and other contexts both to train and inform decision-making agents in organizational and strategic contexts, and also to more realistically predict and best respond to other agents' intent. [ABSTRACT FROM AUTHOR]