A near-infrared-light (NIR)- and UV-light-responsive polymer nanocomposite is synthesized by doping polymer-grafted gold nanorods into azobenzene liquid-crystalline dynamic networks (AuNR-ALCNs). The effects of the two different photoresponsive mechanisms, i.e., the photochemical reaction of azobenzene and the photothermal effect from the surface plasmon resonance of the AuNRs, are investigated by monitoring both the NIR- and UV-light-induced contraction forces of the oriented AuNR-ALCNs. By taking advantage of the material's easy processability, bilayer-structured actuators can be fabricated to display photocontrollable bending/unbending directions, as well as localized actuations through programmed alignment of azobenzene mesogens in selected regions. Versatile and complex motions enabled by the enhanced photocontrol of actuation are demonstrated, including plastic "athletes" that can execute light-controlled push-ups or sit-ups, and a light-driven caterpillar-inspired walker that can crawl forward on a ratcheted substrate at a speed of about 13 mm min . Moreover, the photomechanical effects arising from the two types of light-triggered molecular motion, i.e., the trans-cis photoisomerization and a liquid-crystalline-isotropic phase transition of the azobenzene mesogens, are added up to design a polymer "crane" that is capable of performing light-controlled, robot-like, concerted macroscopic motions including grasping, lifting up, lowering down, and releasing an object.

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Objective: To evaluate the performance of a convolutional neural network (CNN) model that can automatically detect and classify rib fractures, and output structured reports from computed tomography (CT) images. Materials and Methods: This study included 1079 patients (median age, 55 years; men, 718) from three hospitals, between January 2011 and January 2019, who were divided into a monocentric training set (n = 876; median age, 55 years; men, 582), five multicenter/multiparameter validation sets (n = 173; median age, 59 years; men, 118) with different slice thicknesses and image pixels, and a normal control set (n = 30; median age, 53 years; men, 18). Three classifications (fresh, healing, and old fracture) combined with fracture location (corresponding CT layers) were detected automatically and delivered in a structured report. Precision, recall, and F1-score were selected as metrics to measure the optimum CNN model. Detection/diagnosis time, precision, and sensitivity were employed to compare the diagnostic efficiency of the structured report and that of experienced radiologists. Results: A total of 25054 annotations (fresh fracture, 10089; healing fracture, 10922; old fracture, 4043) were labelled for training (18584) and validation (6470). The detection efficiency was higher for fresh fractures and healing fractures than for old fractures (F1-scores, 0.849, 0.856, 0.770, respectively, p = 0.023 for each), and the robustness of the model was good in the five multicenter/multiparameter validation sets (all mean F1-scores > 0.8 except validation set 5 [512 x 512 pixels; F1-score = 0.757]). The precision of the five radiologists improved from 80.3% to 91.1%, and the sensitivity increased from 62.4% to 86.3% with artificial intelligence-assisted diagnosis. On average, the diagnosis time of the radiologists was reduced by 73.9 seconds. Conclusion: Our CNN model for automatic rib fracture detection could assist radiologists in improving diagnostic efficiency, reducing diagnosis time and radiologists' workload.

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In this paper we propose an approximation algorithm for scheduling malleable tasks with precedence constraints. Based on an interesting model for malleable tasks with continuous processor allotments by Prasanna and Musicus [22,23,24], we define two natural assumptions for malleable tasks: the processing time of any malleable task is non-increasing in the number of processors allotted, and the speedup is concave in the number of processors. We show that under these assumptions the work function of any malleable task is nondecreasing in the number of processors and is convex in the processing time.Furthermore, we propose a two-phase approximation algorithm for the scheduling problem. In the first phase we solve a linear program to obtain a fractional allotment for all tasks. By rounding the fractional solution, each malleable task is assigned a number of processors. In the second phase a variant of the list scheduling algorithm is employed. In the phases we use two parameters µ ∈ {1, . . . (m + 1)/2 } and ρ ∈ [0, 1] for the allotment and the rounding, respectively, where m is the number of processors. By choosing appropriate values of the parameters, we show (via a nonlinear program) that the approximation ratio of our algorithm is at most 100/63+100( √ 6469+13)/5481 ≈ 3.291919. We also show that our result is very close to the best asymptotic one.

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