This paper aims at providing researchers and engineering professionals from the first step of solution development to the last step of solution deployment with a practical and comprehensive deep-learning-based solution for detecting construction vehicles. This paper places particular focus on the often-ignored last step of deployment. Our first phase of solution development involved data preparation, model selection, model training, and model validation. Given the necessarily small-scale nature of construction vehicle image datasets, we propose as detection model an improved version of the single shot detector MobileNet, which is suitable for embedded devices. Our study's second phase comprised model optimization, application-specific embedded system selection, economic analysis, and field implementation. Several embedded devices were proposed and compared. Results including a consistent above 90% mean average precision confirm the superior real-time performance of our proposed solutions. Finally, the practical field implementation of our proposed solutions was investigated.This study validates the practicality of deep-learning-based object detection solutions for construction scenarios. Moreover, the detailed information provided by the current study can be employed for several purposes such as safety monitoring, productivity assessments, and managerial decision making.

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In recent years, Intelligent Transportation Systems (ITS) have seen efficient and faster development by implementing deep learning techniques in problem domains which were previously addressed using analytical or statistical solutions and also in some areas that were untouched. These improvements have facilitated traffic management and traffic planning, increased safety and security in transit roads, decreased costs of maintenance, optimized public transportation and ride-sharing company's performance, and advanced driver-less vehicle development to a new stage. This papers primary objective was to provide a review and comprehensive insight into the applications of deep learning models on intelligent transportation systems accompanied by presenting the progress of ITS research due to deep learning. First, different techniques of deep learning and their state-of-the-art are discussed, followed by an in-depth analysis and explanation of the current applications of these techniques in transportation systems. This enumeration of deep learning on ITS highlights its significance in the domain. The applications are furthermore categorized based on the gap they are trying to address. Finally, different embedded systems for deployment of these techniques are investigated and their advantages and weaknesses over each other are discussed. Based on this systematic review, credible benefits of deep learning models on ITS are demonstrated and directions for future research are discussed.

Volumetric liquid side mass transfer, bubble size distribution, and bubble shape was measured in a vertically oriented semi-batch gas-liquid Taylor-Couette vortex reactor with an aspect ratio of Γ=h/(r o -r i )=40 and radius ratio of η=r i /r o =0.75. Azimuthal Reynolds number, Axial Reynolds number and Capillary number were varied between 0 to 3.8×10 4 , 7 to 99, and 6.1×10 -6 to 76.0×10 -6 , respectively. Power-law correlations based on these data were presented for dimensionless Sauter mean diameter and Sherwood number in terms of the dimensionless parameters. Presence of ethanol as surfactant in the liquid was shown to inhibit bubble coalescence, which in addition to lowering interfacial surface tension causes a generally smaller bubble size at higher concentrations of surfactant. It was also shown that the mass transfer coefficient generally increases with higher concentrations of surfactant. Both the bubble diameter and mass transfer are influenced more at lower concentrations of surfactant, and the introduction of higher amount of ethanol causes a decrease in both parameters. Keywords Gas-liquid mass transfer, Surfactant, Sherwood number, Taylor-Couette vortex bioreactor.  The addition of surfactant decreases bubble size Nomenclature Roman a Specific gas-liquid interfacial surface area [m 2 /m 3 ] C Concentration [mg/L] d Bubble diameter [mm] D Diffusion coefficient [m 2 /s] E Ellipticity of spheroid g Gravitational acceleration [m/s 2 ] h Axial distance from the bottom of reactor [m] k Volumetric mass transfer coefficient [m/s] l Spheroid major diameter [m] m Spheroid minor diameter [m] N Impeller (inner cylinder) rotational speed [rev/s] Q Volumetric flow rate [L/min] r i Outer radius of the inner cylinder [m] r o Inner radius of the outer cylinder [m] u Superficial velocity [m/s] Greek Γ Reactor aspect ratio (Γ=h/(r o -r i )) ε G Gas holdup η Reactor radius ratio (η=r i /r o ) ω Rotational speed of the inner cylinder [rad/s]

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