Data services (i.e., office-on-wheels and entertainment -on-wheels) are expected to become a primary driver in the development of future connected cars. However, the sparse spatial distribution of roadside stationary units (RSUs) along the road renders the downloading of data via Roadside-to-Vehicle (R2V) connections intermittent. As a result, data services, especially for those dealing with large volumes of data, may not achieve a good quality-of-service. In this paper, we propose a multiple vehicles protocol for collaborative data downloading by using network coding. When multiple vehicles that are approaching each other have a common interest in certain data, they can collaboratively download the data from a RSU to significantly reduce their download time. We first derive the probability mass functions of the downloading completion time for random, feedback, network coding based three downloading methods, to quantify the benefits of the proposed scheme. Our analytical derivations show that, compared to random and feedback based methods, the proposed approach can significantly improve the download time and will also remove any need for uplink communications from vehicles to the infrastructure. Moreover, we discuss the cooperative group formation issues, and vehicle to vehicle (V2V) data sharing in detail. Simulation results show that the proposed protocol has a more robust performance compared to random and feedback based schemes. Also, we constitute simulations to show that the proposed scheme can apply to the scenarios with dynamic network topology and unperfect V2V data sharing.

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Background: Breast ultrasound is the first choice for breast tumor diagnosis in China, but the Breast Imaging Reporting and Data System (BI-RADS) categorization routinely used in the clinic often leads to unnecessary biopsy. Radiologists have no ability to predict molecular subtypes with important pathological information that can guide clinical treatment.Materials and Methods: This retrospective study collected breast ultrasound images from two hospitals and formed training, test and external test sets after strict selection, which included 2,822, 707, and 210 ultrasound images, respectively. An optimized deep learning model (DLM) was constructed with the training set, and the performance was verified in both the test set and the external test set. Diagnostic results were compared with the BI-RADS categorization determined by radiologists. We divided breast cancer into different molecular subtypes according to hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) expression. The ability to predict molecular subtypes using the DLM was confirmed in the test set.Results: In the test set, with pathological results as the gold standard, the accuracy, sensitivity and specificity were 85.6, 98.7, and 63.1%, respectively, according to the BI-RADS categorization. The same set achieved an accuracy, sensitivity, and specificity of 89.7, 91.3, and 86.9%, respectively, when using the DLM. For the test set, the area under the curve (AUC) was 0.96. For the external test set, the AUC was 0.90. The diagnostic accuracy was 92.86% with the DLM in BI-RADS 4a patients. Approximately 70.76% of the cases were judged as benign tumors. Unnecessary biopsy was theoretically reduced by 67.86%. However, the false negative rate was 10.4%. A good prediction effect was shown for the molecular subtypes of breast cancer with the DLM. The AUC were 0.864, 0.811, and 0.837 for the triple-negative subtype, HER2 (+) subtype and HR (+) subtype predictions, respectively.Conclusion: This study showed that the DLM was highly accurate in recognizing breast tumors from ultrasound images. Thus, the DLM can greatly reduce the incidence of unnecessary biopsy, especially for patients with BI-RADS 4a. In addition, the predictive ability of this model for molecular subtypes was satisfactory,which has specific clinical application value.

An optimization method based on a regression model was established by combining physical experiments, and an extended distinct element method (EDEM) simulation was proposed to address the difficult problem of obtaining the contact characteristic parameters used in the discrete element method (DEM) model of quinoa grains and for calibrating the parameters of the quinoa DEM model. The Plackett-Burman test was designed using Design-Expert software to screen the parameters of the quinoa DEM model, and the quinoa-quinoa static friction coefficient, quinoa-polylactic acid (PLA) static friction coefficient and quinoa-quinoa rolling friction coefficient were found to have significant effects on the repose angle. The optimal value intervals of the parameters with a significant impact on the repose angle were determined using the steepest ascent test. A regression model of the repose angle and the parameters with a significant impact on the repose angle was then established with the Box-Behnken design and further optimized, and the combination of optimal parameters was as follows: 0.26 for the quinoa-quinoa static friction coefficient (E), 0.38 for the quinoa-PLA static friction coefficient (F), and 0.08 for the quinoa-quinoa rolling friction coefficient (G). Lastly, the optimal combination was used in the verification performed by the DEM simulation, and the error between the simulated repose angle and the target repose angle was 0.86%. These findings indicated that it was feasible to use the response surface optimization to calibrate the parameters required for quinoa DEM simulation and that the combination of optimal parameters can provide a reference for selecting the characteristic contact parameters used in quinoa DEM simulation.

Heuristic molecular lipophilicity potential (HMLP) is applied in the study of lipophilicity and hydrophilicity of 20 natural amino acids side chains. The HMLP parameters, surface area S(i), lipophilic indices L(i), and hydrophilic indices H(i) of amino acid side chains are derived from lipophilicity potential L(r). The parameters are correlated with the experimental data of phase-transferring free energies of vapor-to-water, vapor-to-cyclohexane, vapor-to-octanol, cyclohexane-to-water, octanol-to-water, and cyclohexane-to-octanol through a linear free energy equation DeltaG(0)(tr,i) = b(0) + b(1)S(i) (+) + b(2)S(i) (-) + b(3)L(i) + b(4)H(i). For all above six phase-transfer free energies, the HMLP parameters of 20 amino acid side chains give good calculation results using linear free energy equation. HMLP is an ab initio quantum chemical approach and a structure-based technique. Except for atomic van der Waals radii, there are no other empirical parameters used. The HMLP has clear physical and chemical meaning and provides useful lipophilic and hydrophilic parameters for the studies of proteins and peptides.