The maize weevil (Sitophilus zeamais) is one of the main insect responsible of significant losses in stored products, and to keep nutritional value of them to find effective and safe solutions are very important. The Hypericum genus might be a potential source of new bio-insecticides due to the chemical composition of essential oils. In this study, components of essential oils of three Hypericum species were investigated for first time by Gas Chromatography-Mass Spectrometry (GC-MS) and, fumigant and contact toxicities as well as the repellent activity of essential oils of them were evaluated against S. zeamais adults. While the main components in H. mexicanum oil were determined as n-nonane (53.08%) and α-pinene (25.28%), the major constituents were determined as α-pinene (45.52%) and β-caryophyllene (13.59%) in the essential oil of H. myricariifolium. Chemical composition of essential oil of H. juniperinum were found to be n-nonane (12.0%), α-pinene (8.25%), geranyl acetate (7.93%), and β-caryophyllene (13.60%). The results revealed that H. mexicanum and H. myricariifolium oils have fumigant toxicity (LC50 < 500 µL/L air) and a potential action as repellents (RP > 70% at 6.2–22.7 μL/L air) for the control of the pest.
The use of imagined speech with electroencephalographic (EEG) signals is a promising field of brain-computer interfaces (BCI) that seeks communication between areas of the cerebral cortex related to language and devices or machines. However, the complexity of this brain process makes the analysis and classification of this type of signals a relevant topic of research. The goals of this study were: to develop a new algorithm based on Deep Learning (DL), referred to as CNNeeg1-1, to recognize EEG signals in imagined vowel tasks; to create an imagined speech database with 50 subjects specialized in imagined vowels from the Spanish language (/a/,/e/,/i/,/o/,/u/); and to contrast the performance of the CNNeeg1-1 algorithm with the DL Shallow CNN and EEGNet benchmark algorithms using an open access database (BD1) and the newly developed database (BD2). In this study, a mixed variance analysis of variance was conducted to assess the intra-subject and inter-subject training of the proposed algorithms. The results show that for intra-subject training analysis, the best performance among the Shallow CNN, EEGNet, and CNNeeg1-1 methods in classifying imagined vowels (/a/,/e/,/i/,/o/,/u/) was exhibited by CNNeeg1-1, with an accuracy of 65.62% for BD1 database and 85.66% for BD2 database.
The introduction of the blue-noise spectra-high-frequency white noise with minimal energy at low frequencies-has had a profound impact on digital halftoning for binary display devices, such as inkjet printers, because it represents an optimal distribution of black and white pixels producing the illusion of a given shade of gray. The blue-noise model, however, does not directly translate to printing with multiple ink intensities. New multilevel printing and display technologies require the development of corresponding quantization algorithms for continuous tone images, namely multitoning. In order to define an optimal distribution of multitone pixels, this paper develops the theory and design of multitone, blue-noise dithering. Here, arbitrary multitone dot patterns are modeled as a layered superposition of stack-constrained binary patterns. Multitone blue-noise exhibits minimum energy at low frequencies and a staircase-like, ascending, spectral pattern at higher frequencies. The optimum spectral profile is described by a set of principal frequencies and amplitudes whose calculation requires the definition of a spectral coherence structure governing the interaction between patterns of dots of different intensities. Efficient algorithms for the generation of multitone, blue-noise dither patterns are also introduced.
In this paper, an analysis of the emissions of an LED lamp is performed, when it is connected in parallel with electrical devices of linear and non-linear behavior. The objective is to determine if the presence of other loads connected in the same installation can cause a change in the emissions of the LED lamp. A measurement assembly and a test electrical network are built to perform the measurements of voltage and current emissions generated by the LED lamp between 1 Hz and 250 kHz. Incandescent, CFL and LED lamps of different power and technology are used as test loads. The analysis of results indicates that the increase of devices connected in parallel to an LED bulb can in general reduce the current THD. However, there are components in the current emission that increase their RMS value.
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