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Corn germplasm with different mesocotyl elongation was characterized for High Valleys in Mexico by estimating the general combinatory aptitude (GCA), specific combinatory aptitude (SCA), heterosis (H), inbreeding depression (ID) and principal component aptitude (PCA), with the purpose of directing the improvement for deep sowing. The hypothesis was that the parents and crosses of mesocotyl present variability in seedling and adult plant traits based on deep sowing. The 36 F1 and F2 crosses—derived from nine parents, three with short mesocotyl (S), three medium (M) and three long (L), obtained through Griffing diallel II—plus the parents were planted in sand beds and polyethylene bags in a greenhouse during the spring–summer cycles of 2021 and 2022. The following traits were measured: length of mesocotyl (LM), length of coleoptile, total seedling dry matter and 10 cob traits in addition to total dry matter. In 11 of the 14 traits, there was a positive and significant correlation (p ≤ 0.05) between the GCA of the parents and their LM. The highest SCA, H and ID (p ≤ 0.05) were for crosses L × L for all the traits measured. When comparing the GCA/SCA proportions, this relation varied from 0.76 to 0.97, which points to practically equal additive effects with those of dominance; however, in parents and L × L crosses, this relation was on average 0.94, 1.07 in M × M, 0.22 in S × S and 0.36 in L × S. In both F1 and F2, the variation was explained by two principal components: 89.5% for GCA and 73.4% for SCA. In both generations, the parents with higher GCA were H-48, HS-2 and Promesa, the three with long mesocotyl, while those with the highest GCA were crosses between these three hybrids.

Photosynthesis is one of the essential processes for life on the planet. Photosynthesis cannot be measured directly because this complex process involves different variables; therefore, if some variables of interest are integrated and measured, photosynthesis can be inferred through a mathematical model. This article presents a fuzzy mathematical model to estimate photosynthesis. This approach uses as input variables: Soil moisture, ambient temperature, incident radiation, relative humidity, and leaf temperature. The fuzzy system was trained through data obtained from experiments with jalapeño pepper plants and then validated against the LI-COR Li-6800 equipment. The correlation coefficient (R2) obtained was 0.95, which is a higher value than some published in the literature. Based on the Takagi-Sugeno method, the proposed model was designed and implemented on the MATLAB platform using ANFIS (adaptive neuro-fuzzy inference system) to determine the parameters, thus achieving a high-precision model. In addition, the fuzzy model can predict photosynthesis at different temperature changes, soil moisture levels, and light levels. The results of this study indicate the possibility of modeling photosynthesis using the fuzzy logic technique, whose performance is much higher than other methods published in recent articles.

The active sound navigation and ranging (SONAR) transmission system emits acoustic pulses underwater using a wave generator, a SONAR power amplifier (SPA), and a projector. The acoustic pulse travel in the direction of the target and return as an echo to a hydrophone to learn the range or speed of the object. Often the same device is used as a hydrophone and a projector; in this context, it is known as a transducer. In order to obtain a maximum range of detection in the SONAR, it is desirable to generate the maximum amount of acoustic power until the point in which the echo can be detectable in an atmosphere with non-wished noise. Therefore, a high value of source level (SL) is required that depends largely on the value of electrical power applied to the transducer (Pe). However, when trying to obtain the maximum range of detection in the SONAR system there are the following three peculiar limitations that affect performance: The cavitation, the reverberation, and the effect of interaction in the near field. In this paper, an experimental measurement methodology is presented to detect the cavitation effects in a tonpilz-type transducer for an active SONAR transmission system using a transducer as a projector and a calibrated hydrophone in a hydroacoustic tank by measuring the parameters of total harmonic distortion of the fundamental waveform (THD-F) of the generated acoustic pulse, transmitting voltage response (TVR) to characterize the system and sound pressure level (SPL) that indicates the intensity of sound at a given distance. Whereas the reverberation and the interaction effect in the near field are objects of other study cases. A 570.21 W and THD-F < 5% switched-mode power amplifier (SMPA) prototype was developed to excite the electroacoustic transducer employing a full-bridge inverter (FBI) topology and a digital controller using a field-programmable gate array (FPGA) for unipolar sine pulse width modulation (SPWM) to generate a continuous wave (CW) acoustic pulse at a frequency 11.6 kHz. The results obtained show that from the level of Pe=196.05 W with the transducer at 1 m of depth, the value of THD-F increases significantly while the behavior of the TVR and SPL parameters is affected since it is not as expected and is attributed when cavitation occurs.

Additional degrees of freedom in a fractional-order control strategy for power electronic converters are well received despite the lack of reliable tuning methods. Despite artificial/swarm intelligence techniques have been used to adjust controller parameters to improve more than one characteristic/property at the same time, smart tuning not always leads to realizable structures or reachable parameter values. Thus, adjustment boundaries to ensure controller viability are needed. In this manuscript the fractional-order approach is described in terms of El-Khazali biquadratic module, which produces the lowest order approximation, instead of using a definition. A two-modes controller structure is synthesize depending on uncontrolled plant needs and parameters are adjusted through particle swarm and genetic optimization algorithms for comparison. Two error-based minimization criteria are used to consider output performance into the process. Two restrictions complement the optimization scheme, one seeks to ensure desired robustness while the other prevents from synthesizing a high-gain controller. Optimization results showed similarity between minima obtained and significant difference between parameters of those controller optimized without the proposed constraints was determined. Numerical and experimental results are provide to validate proposed approach effectiveness. Effective regulation, good tracking characteristic and robustness in the presence of load variations are the main results.

Photosynthesis is a vital process for the planet. Its estimation involves the measurement of different variables and its processing through a mathematical model. This article presents a black-box mathematical model to estimate the net photosynthesis and its digital implementation. The model uses variables such as: leaf temperature, relative leaf humidity, and incident radiation. The model was elaborated with obtained data from Capsicum annuum L. plants and calibrated using genetic algorithms. The model was validated with Capsicum annuum L. and Capsicum chinense Jacq. plants, achieving average errors of 3% in Capsicum annuum L. and 18.4% in Capsicum chinense Jacq. The error in Capsicum chinense Jacq. was due to the different experimental conditions. According to evaluation, all correlation coefficients (Rho) are greater than 0.98, resulting from the comparison with the LI-COR Li-6800 equipment. The digital implementation consists of an FPGA for data acquisition and processing, as well as a Raspberry Pi for IoT and in situ interfaces; thus, generating a useful net photosynthesis device with non-invasive sensors. This proposal presents an innovative, portable, and low-scale way to estimate the photosynthetic process in vivo, in situ, and in vitro, using non-invasive techniques.

This paper introduces a flexible convolver capable of adapting to the different convolution layer configurations of state-of-the-art Convolution Neural Networks (CNNs). The use of two proposed programmable components achieves this adaptability. A Programmable Line Buffer (PLB) based on Programmable Shift Registers (PSRs) allows the generation of the required convolution masks required for each processed CNN layer. The convolution layer computing is performed through a proposed programmable systolic array configured according to the target device resources. In order to maximize the device resource usage and to achieve a shortened processing time, the filter, data, and loop parallelisms are leveraged. These characteristics allow the described architecture to be scalable and implemented on any FPGA device targeting different applications. The convolver description was written in VHDL using the Intel Cyclone V 5CSXFC6D6F31C6N device as a reference. The experimental results show that the proposed computing method allows the processing of any CNN without requiring special adaptation for a specific application since the standard convolution algorithm is used. The proposed flexible convolver achieves competitive performance compared with those reported in related works.

Photosynthesis is a process that indicates the productivity of crops. The estimation of this variable can be achieved through methods based on mathematical models. Mathematical models are usually classified as empirical, mechanistic, and hybrid. To mathematically model photosynthesis, it is essential to know: the input/output variables and their units; the modeling to be used based on its classification (empirical, mechanistic, or hybrid); existing measurement methods and their invasiveness; the validation shapes and the plant species required for experimentation. Until now, a collection of such information in a single reference has not been found in the literature, so the objective of this manuscript is to analyze the most relevant mathematical models for the photosynthesis estimation and discuss their formulation, complexity, validation, number of samples, units of the input/output variables, and invasiveness in the estimation method. According to the state of the art reviewed here, 67% of the photosynthesis measurement models are mechanistic, 13% are empirical and 20% hybrid. These models estimate gross photosynthesis, net photosynthesis, photosynthesis rate, biomass, or carbon assimilation. Therefore, this review provides an update on the state of research and mathematical modeling of photosynthesis.

(1) Background: As breast cancer studies suggest, a high percentage of breast density (PBD) may be related to breast cancer incidence. Although PBD screening is one of the strongest predictors of breast cancer risk, X-ray-based mammography evaluation is subjective. Therefore, new objective PBD measuring techniques are of interest. A case study analyzing the PBD of thirteen female participants using a bioimpedance-based method, the anomalies tracking circle (ATC), is described in this paper. (2) Methods: In the first stage, the breast bioimpedance of each participant was measured. Then, the participant breast density was determined by applying a mammogram just after the breast bioimpedance measurement stage. In the third stage, the ATC algorithm was applied to the measured bioimpedance data for each participant, and a results analysis was done. (3) Results: An ATC variation according to the breast density was observed from the obtained data, this allowed the use of classification techniques to determine the PBD. (4) Conclusions: The described breast density method is a promising approach that might be applied as an auxiliary tool to the mammography in order to obtain precise and objective results for evaluation of breast density and with that determine potential breast cancer risk.

Optical systems are used for analysing the internal composition and the external properties in food. The measurement of the lycopene content in fruits and vegetables is important because of its benefits to human health. Lycopene prevents cardiovascular diseases, cataracts, cancer, osteoporosis, male infertility, and peritonitis. Among the optical systems focused on the estimation and identification of lycopene molecule are high-performance liquid chromatography (HPLC), the colorimeter, infrared near NIR spectroscopy, UV-VIS spectroscopy, Raman spectroscopy, and the systems of multispectral imaging (MSI) and hyperspectral imaging (HSI). The main objective of this paper is to present a review of the current state of optical systems used to measure lycopene in fruits. It also reports important factors to be considered in order to improve the design and implementation of those optical systems. Finally, it was observed that measurements with HPLC and spectrophotometry present the best results but use toxic solvents and require specialized personnel for their use. Moreover, another widely used technique is colorimetry, which correlates the lycopene content using color descriptors, typically those of CIELAB. Likewise, it was identified that spectroscopic techniques and multispectral images are gaining importance because they are fast and non-invasive.

The elongation of the mesocotyl and the coleoptile and other seedling traits were analyzed from 16 hybrids of two seed sizes, five varieties and a control. Sowing was conducted in sand beds during the S-F 2020 cycle, where nine genotypes were identified that differed in the elongation of the mesocotyl: long (H-48, HS-2 and Promesa); medium (H-44-H-52 and H-70); and short (H-49 AE, H-40 and H-32). A total of 36 possible crosses were obtained between these nine parents, which were established in the S-S 2021 cycle, and on sand beds. Results show that seed size affected (p< 0.05) the speed and percentage of emergence, the elongation of mesocotyl–coleoptile, the biomass and the heterosis in parents and their crosses. The H-48 hybrid presented greater speed and percentage of emergence and elongation of the mesocotyl and the coleoptile with both seed sizes. The highest dry weight of mesocotyl, coleoptile, roots, and leaves was found in the hybrids Promesa and H-48. The crosses between parents with contrasting mesocotyl presented superior elongation and dry weight (p ≤ 0.05) compared to their parents, with the long × long (1 × 2, 1 × 3 and 2 × 3) crosses standing out for all the traits measured. A strong positive association was obtained (p ≤ 0.01) between the elongation of the mesocotyl–coleoptile, the percentage of emergence, and the production of total dry matter in parents and their crosses.