A common objective in mobile robotics is to find an optimal trajectory to reach a final destination from a starting point location. Obstacles are likely to be encountered in the robot's trajectory that need to be avoided. In this article, a path planning approach for mobile robots that uses the algorithms of potential field in conjunction with Local Minimal Avoidance (LMA) is presented in order to avoid local minima and to achieve an optimal robot's navigation.

Anthropogenic activities, such as agricultural, industrial, and domestic, generate wastewater, leading to environmental concerns. Wastewater constituents (organic matter, pathogens, pharmaceuticals, heavy metals, and nutrients) have a negative impact if not treated, harming ecosystems and human health. Phytoremediator plants are a good option for domestic wastewater treatment since they help remove pollutants through their physiological activities, which are highly related to anatomical adaptations due to their growth in humid habitats. Macrophytes are a useful tool when coupled with a bioelectrochemical constructed wetland and MFC (CW-MFC), which can enhance the removal efficiency of organic matter present in wastewater and promote higher bioelectricity due to the root activity of plants. This review aims to compare different aquatic macrophyte types in wastewater treatment efficiency and provide useful information for plant selection.

Microbial fuel cells (MFCs) can have high pollutant removal efficiencies and generate electricity; however, the use of selective membranes represents a considerable expense. In this investigation, the performance of a membraneless MFC was evaluated at different hydraulic retention times (HRTs) of 12, 24, 36, and 48 h. The chemical oxygen demand removal efficiencies (CODREs) were 93.5, 90.9, 87.3, and 85.4%, and the biochemical oxygen demand (BODRE) values were 94.5, 91.5, 88.9, and 85.5 at HRTs of 48, 36, 24, and 12 h, respectively. Lower concentrations of solids (suspended solids and total dissolved solids), total nitrogen, phosphorus, fats and oils, and microbiological contamination (helminth eggs and fecal coliforms) were detected when operating the system at a 48 h HRT. At an HRT of 12 h, no decrease in electrical conductivity was detected, whereas at 48 h, it decreased by 19.6%. The oxidation–reduction potential and OCV increased at longer HRTs. The microorganisms detected at the anode were Achromobacter denitrificans, Achromobacter anxifer, and Pseudomonas aeruginosa. The 48 h HRT improved the chemical, physical, and microbiological quality of the municipal wastewater, favoring voltage generation.

The development of a virtual scenario is desirable in a number of hostile environments such as in mining, or sewer pipelines to inspect its current condition and to identify possible failures. Obtaining a virtual image through the reconstruction of the mineshaft or inner pipe is a valuable tool for assessment and fault detection. In this paper, we present results for our 3D reconstruction approach, which proposes to employ regions with affine transformations that are used as dense correspondences. These features are calculated through local correspondences, such as the SIFT (Scale-invariant feature transform) and SURF (Speeded Up Robust Features). The correspondences are required to obtain the orientation and position of the camera and to get the structure of the scene leading to the creation of the 3D environment. The algorithm is presented as well as its results, which highlights the potential of the method.

In this article, the implementation of a machine vision algorithm into a mobile navigation robot for object recognition is presented. By using its color and machine vision algorithms, such as Canny and the Hough transform as a complement in order, improves the recognition of the selected object. These algorithms is implemented in a device with Android Operating System as an application (Apps).

A parallel bacterial foraging algorithm was developed for the multiple sequence alignment problem. Four sets of homologous genetic and protein sequences related to Alzheimer’s disease among various species were collected from the NCBI database for convergence analysis and performance comparison. The main question was the following: is the bacterial foraging algorithm suitable for the multiple sequence alignment problem? Three versions of the algorithm were contrasted by performing a t-test and Mann–Whitney test based on the results of a 30-run scheme, focusing on fitness, execution time, and the number of function evaluations as performance metrics. Additionally, we conducted a performance comparison of the developed algorithm with the well-known Genetic Algorithm. The results demonstrated the consistent efficiency of the bacterial foraging algorithm, while the version of the algorithm based on gap deletion presented an increased number of function evaluations and excessive execution time. Overall, the first version of the developed algorithm was found to outperform the second version, based on its efficiency. Finally, we found that the third bacterial foraging algorithm version outperformed the Genetic Algorithm in the third phase of the experiment. The sequence sets, the algorithm’s Python 3.12 code and pseudocode, the data collected from the executions, and a GIF animation of the convergence on various different sets are available for download.