Infrastructural review of the distributed telecommunication system of road traffic and its protocols

To organize an efficient transport structure, modern road telecommunication systems provide information collection about the vehicle connected to the system and analyze it. The modern car in such a system is considered to be connected. Such information systems can collect information about the vehicle. This information includes its driving parameters, location, and the parameters of the vehicle systems state. After processing and analyzing this information, it is possible to form recommendations and control actions. These recommendations are used by the driver or an automated vehicle control system. This article describes the general principle of the operation of modern transport telecommunication systems. The car-to-car type of interaction protocols are highlighted in this system. Wireless communication technologies that allow this interaction to be implemented are described. One of the principles was developed, according to which the system can determine the optimal use of the vehicle resource and the aggressiveness of the driving style of a freight vehicle on the basis of an automated algorithm for issuing recommendations for driver actions. This principle is considered as exemplified by a series of load characteristics of a diesel engine. The principle of choosing the optimal series of recommendations to a group of drivers to optimize the movement of traffic through the car-tocar interaction has been formulated.

Parametric Methodology to Optimize the Sizing of Solar Collector Fields in Series-Parallel Arrays

The analysis of solar thermal systems through numerical simulation is of great importance, since it allows predicting the performance of many configurations in any location and under different climatic conditions. Most of the simulation tools are commercial and require different degrees of training; therefore, it is important to develop simple and reliable methodologies to obtain similar results. This study presents a parametric methodology to size stationary solar collector fields, with operating temperatures up to 150 °C. The costs of the collector loop piping and the pumping power of different series–parallel arrays is considered. The proposed tool was validated with experimental data and through simulations using commercial software. The tool allows establishing series–parallel arrays and calculates the volume of the storage tank according to the thermal load. The calculation is based on the system energy balance, where the mass flow and the heat losses in the interconnections of the collectors are taken into account. The number of collectors and the optimal series–parallel array were determined. The results show deviations lower than 7% in the relative error of the temperature profiles and in the solar fraction, with respect to the results obtained by dynamic simulations.

High-throughput image labeling and quality control for clinical trials using machine learning

<p class="abstract"><strong>Background:</strong> Manually importing and analyzing image data can be time-consuming, prone to human error, and costly for large clinical trial datasets. This can lead to delays in quality control (QC) feedback to imaging sites and in obtaining data analysis results. Herein we describe the creation and application of a high-throughput review process for import, classification, labeling and QC of large multimodal clinical trial image datasets.</p><p class="abstract"><strong>Methods:</strong> Automated methods were used to remove patient identifying information, extract image header data, and filter image data for usability. A convolutional neural net was applied to estimate anatomy for CT images. Internal scores were assigned for each image series to identify the optimal series for labeling and reading of each anatomical region. Image QC reports were automatically generated for all patients.</p><p class="abstract"><strong>Results:</strong> In combined studies for which 204,492 series were received, 27,841 series were identified as usable and 13,415 series were labeled. Using this high-throughput method, total work-hours required per time point were reduced by an approximate factor of ten when compared to traditional review and labeling methods. Our anatomic classification system identified 95.7% of image series correctly, with the remaining series being manually corrected before labeling and analysis.</p><p class="abstract"><strong>Conclusions: </strong>A high-throughput image analysis pipeline was implemented in a large combined dataset of clinical trial image series. This pipeline can be applied across other studies and modalities for fast image data characterization, labeling and QC.</p>

Resource-Driven Mission-Phasing Techniques for Constrained Agents in Stochastic Environments

Because an agent's resources dictate what actions it can possibly take, it should plan which resources it holds over time carefully, considering its inherent limitations (such as power or payload restrictions), the competing needs of other agents for the same resources, and the stochastic nature of the environment. Such agents can, in general, achieve more of their objectives if they can use --- and even create --- opportunities to change which resources they hold at various times. Driven by resource constraints, the agents could break their overall missions into an optimal series of phases, optimally reconfiguring their resources at each phase, and optimally using their assigned resources in each phase, given their knowledge of the stochastic environment. In this paper, we formally define and analyze this constrained, sequential optimization problem in both the single-agent and multi-agent contexts. We present a family of mixed integer linear programming (MILP) formulations of this problem that can optimally create phases (when phases are not predefined) accounting for costs and limitations in phase creation. Because our formulations multaneously also find the optimal allocations of resources at each phase and the optimal policies for using the allocated resources at each phase, they exploit structure across these coupled problems. This allows them to find solutions significantly faster(orders of magnitude faster in larger problems) than alternative solution techniques, as we demonstrate empirically.