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Models capable of capturing and reproducing the variability observed in experimental trials can be valuable for planning and control in the presence of uncertainty. This paper reports on a new data-driven methodology that extends deterministic models to a stochastic regime and offers probabilistic guarantees of model fidelity. From an acceptable deterministic model, a stochastic one is generated, capable of capturing and reproducing uncertain system–environment interactions at given levels of fidelity. The reported approach combines methodological elements from probabilistic model validation and randomized algorithms, to simultaneously quantify the fidelity of a model and tune the distribution of random parameters in the corresponding stochastic extension, in order to reproduce the variability observed experimentally in the physical process of interest. The approach can be applied to an array of physical processes, the models of which may come in different forms, including differential equations; we demonstrate this point by considering examples from the areas of miniature legged robots and aerial vehicles.

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Dormant pruning of fruit trees is one of the most costly and labor‐intensive activities in specialty crop production. We present a system that solves the first step in the process of automated pruning: accurately measuring and modeling the fruit trees. Our system employs a laser sensor to collect observations of fruit trees from multiple perspectives, and it uses these observations to measure parameters needed for pruning. A split‐and‐merge clustering algorithm divides the collected data into three sets of points: trunk candidates, junction point candidates, and branches. The trunk candidates and junction point candidates are then further refined by a robust fitting algorithm that models as cylinders each segment of the trunk and primary branches. In this work, we focus on measuring the diameters of the primary branches and the trunk, which are important factors in dormant pruning and can be obtained directly from the cylindrical models. We show that the results are qualitatively satisfactory using synthetic and real data. Our experiments with three synthetic and three real apple trees of two different varieties showed that the system is able to identify the primary branches with an average accuracy of 98% and estimate their diameters with an average error of 0.6 cm. Although the current implementation of the system is too slow for large‐scale practical applications (it can measure approximately two trees per hour), our study shows that the proposed approach may serve as a fundamental building block of robotic pruners in the near future.

This paper approaches from an optimal control perspective the problem of fixed-time detection of mobile radioactive sources in transit by means of a collection of mobile sensors. Under simplifying assumptions on the motion and geometry of the source, the sensors, and the surrounding environment, the optimal control problem admits an intuitive, analytic closed-form solution. This solution is obtained thanks to analytic expressions for bounds on the probabilities of detection and false alarm for a Neyman-Pearson detection test. The intuition derived from this analytic solution supports the development of a motion control law that steers (suboptimally) the sensors to a given neighborhood of the suspected source, while navigating among stationary obstacles in their environment. This motion controller closes the loop at the acceleration level of a heterogeneous collection of sensor platforms. Experimental studies with these platforms corroborate the theoretical convergence results.

9000 Background: CCSK was initially described by its bone metastasizing tendencies and propensity for late recurrences. Outcome for patients with CCSK has improved from NWTS 1–4. On NWTS 4 patients were randomized to treatment for 15 months vs 6 months. Their overall 8 year relapse free survival was 88% vs 61%, respectively. NWTS-5 was designed to improve the event free survival (EFS) and overall survival (S) for patients with CCSK by incorporating cyclophosphamide and etoposide. Methods: Prospective single-arm study conducted between August, 1995 and June, 2002. Patients less than 16 years of age with a centrally confirmed pathological diagnosis of CCSK were eligible. Staging consisted of CT scans of chest, abdomen, pelvis, bone scan, skeletal survey, and CT or MRI of head. Patients were treated with vincristine/doxorubicin/cyclophosphamide alternating with cyclophosphamide/etoposide for 24 weeks and XRT (10.8 cGy). Results: 110 eligible patients were enrolled on study. Median age was 22 months, 69% were males, and 63% white. Stage distribution was: stage I, 14; II, 41; III, 46; IV, 9 [metastatic sites: lung (3), bone (1), brain (1), liver (1), bone and bilateral lung (1) and other (2)] Median follow-up is 4.6 years. 5-year EFS and S were 79% (95% CI, 69% to 86%) and 89% (95% CI, 80% to 94%). All but one of 21 recurrences occurred within 3 years of initial treatment. The most common site of recurrence was brain (11/21). 5-year EFS for Stage I-IV was 100%, 87%, 74% and 36% respectively. Adverse prognostic factors for patients with Stage II/III disease were white race, and lymph node involvement. Conclusions: Outcome for patients with CCSK treated on NWTS-5 is similar to that seen on NWTS-4 and recent SIOP and UKCCSG trials. Stage is highly predictive of outcome. Brain recurrence was higher than that seen on NWTS-4; lung recurrences were lower. The next Childrens Oncology Group (COG) protocol will use the same treatment for patients with Stages I-III; therapy for patients with Stage IV disease will incorporate carboplatin. The role of XRT in CCSK needs to be evaluated. No significant financial relationships to disclose.

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The use of 3D plant models for high-throughput phenotyping is increasingly becoming a preferred method for many plant science researchers. Numerous camera-based imaging systems and reconstruction algorithms have been developed for the 3D reconstruction of plants. However, it is still challenging to build an imaging system with high-quality results at a low cost. Useful comparative information for existing imaging systems and their improvements is also limited, making it challenging for researchers to make data-based selections. The objective of this study is to explore the possible solutions to address these issues. We introduce two novel systems for plants of various sizes, as well as a pipeline to generate high-quality 3D point clouds and meshes. The higher accuracy and efficiency of the proposed systems make it a potentially valuable tool for enhancing high-throughput phenotyping by integrating 3D traits for increased resolution and measuring traits that are not amenable to 2D imaging approaches. The study shows that the phenotype traits derived from the 3D models are highly correlated with manually measured phenotypic traits (R2 > 0.91). Moreover, we present a systematic analysis of different settings of the imaging systems and a comparison with the traditional system, which provide recommendations for plant scientists to improve the accuracy of 3D construction. In summary, our proposed imaging systems are suggested for 3D reconstruction of plants. Moreover, the analysis results of the different settings in this paper can be used for designing new customized imaging systems and improving their accuracy.