Technical Efficiency of the Subsurface Drainage on Agricultural Lands in the Moldova River Meadow

This study aims to investigate the technical efficiency of different subsurface drainage variants, in terms of the depth of the tile drains, spacing between the drain lines, type and thickness of the drain + filter complex, and the improvement procedures. Within the four variants, the discharge rate of the soil moisture excess was studied. In variants A and D, the spacing between drains is 20 m, and in the variants B and E, the spacing is 15 m. The depth of the tile drains is 0.8 m in variants D and E and 1.0 m in variants A and B. In variant A, tile drainage was combined with land shaping in the bedding system with top of ridges and furrows. Soil moisture was determined on checkpoints placed on drain cross section, at 2 m from drain lines, and of the middle of the drain spacing. In the version with land shaping, the drain lines located under the furrows favor the excess moisture removal. A similar technical efficiency was recorded in unimproved variant but with spacing between drains of 15 m. Best efficiency at removing excess water was registered in variant of the filtering material from ballast associated with flax strains

Assistive Autonomous Mobile Robot Identifies and Retrieves Target Objects

This engineering article presents an original method of building and operating an assistive autonomous mobile robot based on a custom-designed LEGO® structure using Raspberry Pi® Model B computers, Dexter Industries® BrickPi boards, and LEGO NXT peripherals. The robot is programmed using the Python language to detect, identify, and handle objects in places that are inaccessible or dangerous to humans. The robot assists people with limited mobility to find and retrieve hidden or lost objects. The two distinct modes of operation are the assistive autonomous mode, and the exploratory, operator-controlled mode, respectively. In the autonomous mode, the robot moves automatically and uses its pre-programmed input parameters and signals from the ultrasonic sensors and video camera to navigate by avoiding obstacles; upon detecting the target object, the motion ceases, the robotic arm extends, grasps, and retrieves the object. The operator can afterwards direct the robot towards other zones for exploration or object retrieval. In the exploratory mode, the operator controls the movement of the robot and visualizes on a monitor the images continuously sent by the on-board video camera. Future development may consist of implementing the autonomous mode in which objects in motion will be tracked and retrieved

Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights

Reducing traffic accidents is an important public safety challenge, therefore, accident analysis and prediction has been a topic of much research over the past few decades. Using small-scale datasets with limited coverage, being dependent on extensive set of data, and being not applicable for real-time purposes are the important shortcomings of the existing studies. To address these challenges, we propose a new solution for real-time traffic accident prediction using easy-to-obtain, but sparse data. Our solution relies on a deep-neural-network model (which we have named DAP, for Deep Accident Prediction); which utilizes a variety of data attributes such as traffic events, weather data, points-of-interest, and time. DAP incorporates multiple components including a recurrent (for time-sensitive data), a fully connected (for time-insensitive data), and a trainable embedding component (to capture spatial heterogeneity). To fill the data gap, we have - through a comprehensive process of data collection, integration, and augmentation - created a large-scale publicly available database of accident information named US-Accidents. By employing the US-Accidents dataset and through an extensive set of experiments across several large cities, we have evaluated our proposal against several baselines. Our analysis and results show significant improvements to predict rare accident events. Further, we have shown the impact of traffic information, time, and points-of-interest data for real-time accident prediction.Comment: In Proceedings of the 27th ACM SIGSPATIAL, International Conference on Advances in Geographic Information Systems (2019). arXiv admin note: substantial text overlap with arXiv:1906.0540