Automobiles have increased urban mobility, but traffic accidents have also increased. Therefore, road safety is a significant concern involving academics and government. Transit studies are the main supply for studying road accidents, congestion, and flow traffic, allowing the understanding of traffic flow. They require special equipment (sensors) to measure the car’s speed. With technological advances, artificial intelligence, and videos, it is possible to estimate the speed in real-time without modifying the installed urban infrastructure. We need to employ public databases that provide reliable monocular videos to generate automated traffic studies. The problem of speed estimation with a monocular camera involves synchronizing data recording, tracking, and detecting the vehicles over the road considering the lanes and distance between cars. Usually, a set of constraints are considered, such as camera calibration, flat roads, including methods based on the homography and augmented intrusion lines, patterns or regions, or prior knowledge about the actual dimensions of some of the objects. In this paper, we present a system that generates a dataset from videos recorded from a highway—obtaining 532 samples; we separated the vehicle’s detection by lane, estimating its speed. We use this data set to compare five different statistical methods and three machine learning methods to evaluate their accuracy in estimating the cars’ speed in real-time. Our vehicle estimation requires a feature extraction process using YOLOv3 and Kalman filter to detect and track vehicles. The Linear Regression Model (LRM) yielded the best results obtaining a Mean Absolute Error (MAE) of 1.694 km/h for the center lane and 0.956 km/h for the last lane. The results were compared with several state-of-the-art works, having competitive performance. Hence, LRM is fast estimating speed in real time and does not require high computational resources allowing a future hardware implementation.

One-stage production of carbohydrate-enriched microalgae biomass in wastewater is a promising option to obtain biofuels. Understanding the interaction of water quality parameters such as nutrients, carbon, internal carbohydrates, and microbial composition in the culture is crucial for efficient operation and viable large-scale cultivation. Bioprocess models are an essential tool for studying the simultaneous effect of complex factors on carbohydrate accumulation, optimizing the process, and reducing operational costs. In this sense, we use a dataset obtained from an empirical model that analyzed the accumulation of carbohydrates in a single process (simultaneous growth and accumulation) from real wastewater. In this experiment, there were no ideal conditions (limiting nutrient conditions), but rather these limitations are guaranteed by the operating conditions (hydraulic retention times/nutrient or carbon loads). Thus, the model integrates 18 variables that are affected and not only carbohydrates. The effect of these variables directly influences the accumulation of carbohydrates. Therefore, this paper analyzes artificial intelligence (AI) algorithms to develop a model to forecast biomass production in wastewater treatment systems. Carbohydrates were modeled using five artificial intelligence methods: (1) Artificial Neural Networks (ANNs), (2) Convolutional Neural Networks (CNN), (3) Long Short-Term Memory Network (LSTMs), (4) K-Nearest Neighbors (kNN), and (5) Random Forest (RF)). The AI methods allow learning how several components interact and if their combinations work faster than building the physical experiments over the same period of time. After comparing the five learning models, the CNN-1D model obtained the best results with an MSE (Mean Squared Error) = 0.0028. This result shows that the model adequately approximates the system’s dynamics.

We report the discovery and panchromatic follow-up observations of the young Type Ic supernova (SN Ic) SN 2020oi in M100, a grand-design spiral galaxy at a mere distance of 14 Mpc. We followed up with observations at radio, X-ray, and optical wavelengths from only a few days to several months after explosion. The optical behavior of the supernova is similar to those of other normal SNe Ic. The event was not detected in the X-ray band but our radio observations revealed a bright mJy source ( L ν ≈ 1.2 × 10 27 erg s − 1 Hz − 1 ). Given the relatively small number of stripped envelope SNe for which radio emission is detectable, we used this opportunity to perform a detailed analysis of the comprehensive radio data set we obtained. The radio-emitting electrons initially experience a phase of inverse Compton cooling, which leads to steepening of the spectral index of the radio emission. Our analysis of the cooling frequency points to a large deviation from equipartition at the level of ϵ _e /ϵ _B ≳ 200, similar to a few other cases of stripped envelope SNe. Our modeling of the radio data suggests that the shock wave driven by the SN ejecta into the circumstellar matter (CSM) is moving at ∼ 3 × 10 4 km s − 1 . Assuming a constant mass loss from the stellar progenitor, we find that the mass-loss rate is M ̇ ≈ 1.4 × 10 − 4 M ⊙ yr − 1 for an assumed wind velocity of 1000 km s − 1 . The temporal evolution of the radio emission suggests a radial CSM density structure steeper than the standard r ⁻².

This paper presents a multi-model predictor called Qualitative Multi-Model Predictor Plus (QMMP+) for demand forecast in water distribution networks. QMMP+ is based on the decomposition of the quantitative and qualitative information of the time-series. The quantitative component (i.e., the daily consumption prediction) is forecasted and the pattern mode estimated using a Nearest Neighbor (NN) classifier and a Calendar. The patterns are updated via a simple Moving Average scheme. The NN classifier and the Calendar are executed simultaneously every period and the most suited model for prediction is selected using a probabilistic approach. The proposed solution for water demand forecast is compared against Radial Basis Function Artificial Neural Networks (RBF-ANN), the statistical Autoregressive Integrated Moving Average (ARIMA), and Double Seasonal Holt-Winters (DSHW) approaches, providing the best results when applied to real demand of the Barcelona Water Distribution Network. QMMP+ has demonstrated that the special modelling treatment of water consumption patterns improves the forecasting accuracy.

Efficient management of a drinking water network reduces the economic costs related to water production and transport (pumping). Model predictive control (MPC) is nowadays a quite well-accepted approach for the efficient management of the water networks because it allows formulating the control problem in terms of the optimization of the economic costs. Therefore, short-term forecasts are a key issue in the performance of MPC applied to water distribution networks. However, the short-term horizon demand forecast in a horizon of 24 hours in an hourly based scale presents some challenges as the water consumption can change from one day to another, according to certain patterns of behavior (e.g., holidays and business days). This paper focuses on the problem of forecasting water demand for the next 24 hours. In this work, we propose to use a bank of models instead of a single model. Each model is designed for forecasting one particular hour. Hourly models use artificial neural networks. The architecture design and the training process are performed using genetic algorithms. The proposed approach is assessed using demand data from the Barcelona water network.