Collective Cognition on Global Density in Dynamic Swarm.

Swarm density plays a key role in the performance of a robot swarm, which can be averagely measured by swarm size and the area of a workspace. In some scenarios, the swarm workspace may not be fully or partially observable, or the swarm size may decrease over time due to out-of-battery or faulty individuals during operation. This can result in the average swarm density over the whole workspace being unable to be measured or changed in real-time. The swarm performance may not be optimal due to unknown swarm density. If the swarm density is too low, inter-robot communication will rarely be established, and robot swarm cooperation will not be effective. Meanwhile, a densely-packed swarm compels robots to permanently solve collision avoidance issues rather than performing the main task. To address this issue, in this work, the distributed algorithm for collective cognition on the average global density is proposed. The main idea of the proposed algorithm is to help the swarm make a collective decision on whether the current global density is larger, smaller or approximately equal to the desired density. During the estimation process, the swarm size adjustment is acceptable for the proposed method in order to reach the desired swarm density.

Results of the arterial switch operation for Taussig-Bing variants in the setting of a lower-middle income country: A single-institution experience.

BACKGROUND: This study was conducted to evaluate the surgical results of the arterial switch operation for Taussig-Bing variants, at a single institution in a lower-middle income country. METHODS: Between June 2010 and December 2018, all consecutive patients diagnosed with Taussig-Bing variants who underwent the arterial switch operation and ventricular septal defect closure were included in the study. RESULTS: A total of 72 patients of Taussig-Bing variants underwent arterial switch operation and ventricular septal defect closure. There were 10 early deaths (13.9%) and 2 late deaths (2.8%). Intraoperative ventricular septal defect enlargement (hazard ratio [HR] 7.23, 95% confidence interval [CI] 3.1294-16.7167; p < .001), secondary aortic cross-clamping (HR 28.38, 95% CI 4.8427-166.3484; p < .001), postoperative pneumonia (HR 5.64, 95% CI 1.2724-24.9917; p = .023), and postoperative sepsis (HR 5.28, 95% CI 1.3512-20.6553; p = .017) were risk factors for overall mortality by competing risk analysis. Sixty patients (83.3%) required septoparietal trabeculation division/resection during the arterial switch operation in an attempt to avoid right ventricular outflow tract obstruction. The reoperation rate for right ventricular outflow tract obstruction at last follow up was 6% (three patients). The estimated freedom from reoperation for right ventricular outflow tract obstruction at 1, 5, and 9 years was 98.3%, 91.9%, and 91.9%, respectively. CONCLUSIONS: The results of arterial switch operation for Taussig-Bing variants were satisfactory in the operative setting of a lower-middle income country, and performing extensive septoparietal trabeculation division might reduce the reintervention rate for right ventricular outflow tract obstruction in these patients.

Shape Prediction of Nasal Bones by Digital 2D-Photogrammetry of the Nose Based on Convolution and Back-Propagation Neural Network.

In rhinoplasty, it is necessary to consider the correlation between the anthropometric indicators of the nasal bone, so that it prevents surgical complications and enhances the patient's satisfaction. The penetrating form of high-energy electromagnetic radiation is highly impacted on human health, which has often raised concerns of alternative method for facial analysis. The critical stage to assess nasal morphology is the nasal analysis on its anthropology that is highly reliant on the understanding of the structural features of the nasal radix. For example, the shape and size of nasal bone features, skin thickness, and also body factors aggregated from different facial anthropology values. In medical diagnosis, however, the morphology of the nasal bone is determined manually and significantly relies on the clinician's expertise. Furthermore, the evaluation anthropological keypoint of the nasal bone is nonrepeatable and laborious, also finding widely differ and intralaboratory variability in the results because of facial soft tissue and equipment defects. In order to overcome these problems, we propose specialized convolutional neural network (CNN) architecture to accurately predict nasal measurement based on digital 2D photogrammetry. To boost performance and efficacy, it is deliberately constructed with many layers and different filter sizes, with less filters and optimizing parameters. Through its result, the back-propagation neural network (BPNN) indicated the correlation between differences in human body factors mentioned are height, weight known as body mass index (BMI), age, gender, and the nasal bone dimension of the participant. With full of parameters could the nasal morphology be diagnostic continuously. The model's performance is evaluated on various newest architecture models such as DenseNet, ConvNet, Inception, VGG, and MobileNet. Experiments were directly conducted on different facials. The results show the proposed architecture worked well in terms of nasal properties achieved which utilize four statistical criteria named mean average precision (mAP), mean absolute error (MAE), R-square (R 2), and T-test analyzed. Data has also shown that the nasal shape of Southeast Asians, especially Vietnamese, could be divided into different types in two perspective views. From cadavers for bony datasets, nasal bones can be classified into 2 morphological types in the lateral view which "V" shape was presented by 78.8% and the remains were "S" shape evaluated based on Lazovic (2015). With 2 angular dimension averages are 136.41 ± 7.99 and 104.25 ± 5.95 represented by the nasofrontal angle (g-n-prn) and the nasomental angle (n-prn-sn), respectively. For frontal view, classified by Hwang, Tae-Sun, et al. (2005), nasal morphology of Vietnamese participants could be divided into three types: type A was present in 57.6% and type B was present in 30.3% of the noses. In particular, types C, D, and E were not a common form of Vietnamese which includes the remaining number of participants. In conclusion, the proposed model performed the potential hybrid of CNN and BPNN with its application to give expected accuracy in terms of keypoint localization and nasal morphology regression. Nasal analysis can replace MRI imaging diagnostics that are reflected by the risk to human body.