Light stimulation and biofloc technology can be combined to improve the efficiency and sustainability of tilapia production. A 73-day pilot experiment was conducted to investigate the effect of colored light on growth rates and nutritional composition of the Nile tilapia fingerlings (Oreochromis niloticus) in biofloc systems. The effect of colored light on the nutritional composition of bioflocs as a food source for fish was measured. Three groups were illuminated in addition to natural sunlight with colored light using RGB light emitting diodes (LEDs) with peak wavelengths (λ) of 627.27 nm for red (R), 513.33 nm for green (G), and 451.67 nm for blue (B) light. LED light intensity was constant (0.832 mW / cm 2 ), and had an 18-h photoperiod of light per day throughout the study. The control group was illuminated only with natural sunlight (natural). Tilapia had an average initial weight of 0.242 g. There was a significant effect of colored light on tilapia growth and composition. The R group showed the best growth rate, highest survival, and highest lipid content. The B group showed homogeneous growth with the lowest growth rate and lipid content, but the highest protein level. On the other hand, the biofloc composition was influenced by the green light in the highest content of lipids, protein, and nitrogen-free extract.

Biogas methane content is a relevant variable in anaerobic digestion processing where knowledge of process kinetics or an early indicator of digester failure is needed. The contribution of this work is the development of a novel, simple and low cost automatic carbon dioxide-methane gas sensor based on the solubility of gases in water as the precursor of a sensor for biogas quality monitoring. The device described in this work was used for determining the composition of binary mixtures, such as carbon dioxide-methane, in the range of 0–100%. The design and implementation of a digital signal processor and control system into a low-cost Field Programmable Gate Array (FPGA) platform has permitted the successful application of data acquisition, data distribution and digital data processing, making the construction of a standalone carbon dioxide-methane gas sensor possible.

We present a study of natural language using the recurrence network method. In our approach, the repetition of patterns of characters is evaluated without considering the word structure in written texts from different natural languages. Our dataset comprises 85 ebookseBooks written in 17 different European languages. The similarity between patterns of length m is determined by the Hamming distance and a value r is considered to define a matching between two patterns, i.e., a repetition is defined if the Hamming distance is equal or less than the given threshold value r. In this way, we calculate the adjacency matrix, where a connection between two nodes exists when a matching occurs. Next, the recurrence network is constructed for the texts and some representative network metrics are calculated. Our results show that average values of network density, clustering, and assortativity are larger than their corresponding shuffled versions, while for metrics like such as closeness, both original and random sequences exhibit similar values. Moreover, our calculations show similar average values for density among languages which that belong to the same linguistic family. In addition, the application of a linear discriminant analysis leads to well-separated clusters of family languages based on based on the network-density properties. Finally, we discuss our results in the context of the general characteristics of written texts.

Trucks with semi-trailers are widely used for transportation of goods due their low operation cost, but inherent to these vehicles are some problems such as a poor maneuverability. To minimize the effects of this disadvantage, among other solutions, the incorporation of steerable axles in the semitrailers has been proposed. This paper presents a steering equation, and a fuzzy-logic controller for a semi-trailer automatic forced-steering system to minimize the off-tracking and the total swept path width, resulting in an improvement of vehicle´s maneuverability at low speeds. To accomplish this, the suggested control algorithm considers the articulation angle and parameters such as vehicle speed and direction. The system was tested on an instrumented experimental semi-trailer during various predetermined test maneuvers.

In this paper a methodology to restore gray scale images with pixels polluted by random impulsive noise is presented. Noise is discovered using a criterion based on the white top-hat by reconstruction. Pixels detected as corrupted are restored using an iterative morphological algorithm built with extensive and antiextensive morphological transformations. The proposal is compared with the rank ordered mean filter (ROM) and other morphological transformations reported in the current literature.