Comparison of cascade P-PI controller tuning methods for PMDC motor based on intelligence techniques

In this paper, there are two contributions: The first contribution is to design a robust cascade P-PI controller to control the speed and position of the permanent magnet DC motor (PMDC). The second contribution is to use three methods to tuning the parameter values for this cascade controller by making a comparison between them to obtain the best results to ensure accurate tracking trajectory on the axis to reach the desired position. These methods are the classical method (CM) and it requires some assumptions, the genetic algorithm (GA), and the particle swarm optimization algorithm (PSO). The simulation results show the system becomes unstable after applying the load when using the classical method because it assumes cancellation of the load effect. Also, an overshoot of about 3.763% is observed, and a deviation from the desired position of about 12.03 degrees is observed when using the GA algorithm, while no deviation or overshoot is observed when using the PSO algorithm. Therefore, the PSO algorithm has superiority as compared to the other two methods in improving the performance of the PMDC motor by extracting the best parameters for the cascade P-PI controller to reach the desired position at a regular speed.

Mining the Wheat Grain Proteome

Bread wheat is the most widely cultivated crop worldwide, used in the production of food products and a feed source for animals. Selection tools that can be applied early in the breeding cycle are needed to accelerate genetic gain for increased wheat production while maintaining or improving grain quality if demand from human population growth is to be fulfilled. Proteomics screening assays of wheat flour can assist breeders to select the best performing breeding lines and discard the worst lines. In this study, we optimised a robust LC–MS shotgun quantitative proteomics method to screen thousands of wheat genotypes. Using 6 cultivars and 4 replicates, we tested 3 resuspension ratios (50, 25, and 17 µL/mg), 2 extraction buffers (with urea or guanidine-hydrochloride), 3 sets of proteases (chymotrypsin, Glu-C, and trypsin/Lys-C), and multiple LC settings. Protein identifications by LC–MS/MS were used to select the best parameters. A total 8738 wheat proteins were identified. The best method was validated on an independent set of 96 cultivars and peptides quantities were normalised using sample weights, an internal standard, and quality controls. Data mining tools found particularly useful to explore the flour proteome are presented (UniProt Retrieve/ID mapping tool, KEGG, AgriGO, REVIGO, and Pathway Tools).

Evaluation of the herbicide efficiency on soybean crops

Purpose. To study the effectiveness of soybean weed control systems. Methods. Field and laboratory. Results. It was investigated that the use of Pari herbicide in addition to the impact on weed plants as a result of their direct contact with the product also has a noticeable soil effect, and therefore treatment with a rate of 1.0 l/ha was effective in controlling 88.4% of seedlings weeds. The product showed high selectivity in terms of destruction of both dicotyledonous and monocotyledonous species presented in the experimental site. However, the application of Pari twice with a consumption rate of 0.3 l/ha and on the fourth day + 0.4 l/ha proved to be less effective and 79.0% of weeds died under this scheme. This is most likely due to the difficulty of applying small amounts of the product, in addition, re-application required a second pass of the sprayer, which means that part of the lines lost the protective soil film of the herbicide. We determined the best parameters of plant height in the control treatment, but the indicators of weed control, regardless of the phase of the experiment, were the worst. In addition, in the case of herbicide applications, we obtained values close to the control with slight differences in plant height, which may be due to experimental error. Conclusions. It was determined that the herbicide Pulsar 40 proved to be the most effective in the experiment, so with the application of 0.3 l/ha and on the fourth day + 0.4 l/ha, 86.9% of weeds common in soybean crops died. Among herbicides with a combined seed and soil action, Pari and Fabian were effective in applying them once at a full rate. Fabian herbicide was milder than cultivated plants due to the combined active ingredients and lower dose of imazethapyr compared to Pari herbicide. Therefore, the application of 0.1 kg/ha of Fabian yields 3.11 t/ha of soybean seeds. With the use of exclusively germinating preparations, the application of Pulsar 40 at a dose of 0.3 l/ha on the fourth day + 0.4 l/ha was effective, which allowed to form 3.08 t/ha of soy quenching.

Mixing ReaxFF parameters for transition metal oxides using force-matching method

We present the development of the method for the refitting the ReaxFF parameters for a system consisting of the mixed transition metal oxides. We have tested several methods allowing to calculate the differences between the vectors of the forces acting on atoms obtained from the reference DFT simulation and the parameters-dependent ReaxFF. We conclude that the footrule method yields the best parameters among the investigated options. We then validate the parameters using the system consisting of the hematite supported (TiO2)n clusters. The results indicate the refitted parameters allow to obtain acceptable geometries of the clusters upon MD simulation on the ReaxFF level, and despite the short timescale lead to the stable structures.

DAYLIGHT GLARE PROBABILITY PREDICTION FOR AN OFFICE ROOM

Glare assessments are currently made from High Dynamic Range (HDR) images taken from the Point Of View (POV) and viewing direction of a user. This paper analyses the feasibility to estimate the Daylight Glare Probability (DGP) at the user-level based on machine-learning techniques, sun position and a downward-pointing camera sensor mounted at the ceiling of a simulated office environment. Three different office cases have been considered: an empty room, an empty room with venetian blinds and a furnished room without venetian blinds. The influence of the sun direction has been considered as a parameter to predict the observer DGP. Subsequently, the best parameters have been selected to build a black box model using Artificial Intelligence (AI). Results show that, by using the DGP of the ceiling camera and the sun position, it is possible to accurately predict the DGP for an observer’s POV.

Multiple Fano resonances based on clockwork spring-shaped resonator for refractive index sensing

A surface plasmon polarized structure consisting of two metal-insulator-metal (MIM) waveguide coupled with clockwork spring-shaped resonators are constructed in this paper, and its geometric parameters are controlled within a few hundred nanometers. The finite element method (FEM) and multimode interference coupled mode theory (MICMT) are used to simulate and theoretically calculate the optical response of the designed structure. By modifying the structural parameters of the system, the influence on the asymmetry of the Fano resonance line is studied. The changes of the transmission spectra at different refractive indexes are also investigated. Based on this asymmetric resonant line, the sensitivity and FOM* (figure of merit) value of the cavity with different parameters are measured. The sensitivity and FOM* under the best parameters are 1200 nm/RIU and 191.6, respectively. The surface plasmon structure proposed and the results in this paper are promising for applications in the field of high-performance sensing and micro-nano optical devices.

LCA and LCCA Methods in Apple Orchard Management, Conventional and Organic

The paper aims to establish a framework based on the method of LCA (Life Cycle Assessment) and LCCA (Life Cycle Cost Analysis) in order to select the best parameters of apple technology by identifying the particularities of fruit production technologies and providing practical recommendations on how to approach the two methods. The methodology of the paper is the use of LCA and LCCA methods, as techniques for assessing the environmental elements associated with the technological process and the economic indicators. The paper was meant to be part of a project whose purpose is to develop a decision and management instrument for the fruit farm Istrița, USAMV of Bucharest. The results consist in a technological and economic structure suitable for application to the selected apple tree technologies, in conventional and ecological system.

In Silico Identification of Chikungunya Virus B- and T-Cell Epitopes with High Antigenic Potential for Vaccine Development

Reverse vaccinology is an outstanding strategy to identify antigens with high potential for vaccine development. Different parameters of five prediction programs were used to assess their sensitivity and specificity to identify B-cell epitopes of Chikungunya virus (CHIKV) strains reported in the IEDB database. The results, based on the use of 15 to 20 mer epitopes and the polyproteins to which they belong, were compared to establish the best parameters to optimize the prediction of antigenic peptides of the Mexican strain CHIKV AJV21562.1. LBtope showed the highest specificity when we used the reported epitopes and polyproteins but the worst sensitivity with polyproteins; ABCpred had similar specificity to LBtope only with the epitopes reported and showed moderate specificity when we used polyproteins for the predictions. Because LBtope was more reliable in predicting true epitopes, it was used as a reference program to predict and select six novel epitopes of the Mexican strain of CHIKV according to prediction frequency, viral genome localization, and non-homology with the human proteome. On the other hand, six bioinformatics programs were used with default parameters to predict T-cell epitopes in the CHIKV strains AJV21562.1 and AJV21561.1. The sequences of the polyproteins were analyzed to predict epitopes present in the more frequent HLA alleles of the Mexican population: DQA1*03011, DQA1*0401, DQA1*0501, DQB1*0201, DQB1*0301, DQB1*0302, and DQB1*0402. Fifteen predicted epitopes in the non-structural and 15 predicted epitopes in the structural polyprotein (9- to 16-mers) with the highest scores of each allele were compared to select epitopes with at least 80% identity. Next, the epitopes predicted with at least two programs were aligned to the human proteome, and 12 sequences without identity with the human proteome were identified as potential antigenic candidates. This strategy would be useful to evaluate vaccine candidates against other viral diseases affecting the countries of the Americas and to increase knowledge about these diseases.

Mixing ReaxFF Parameters for Transition Metal Oxides Using Force-Matching Method.

We present the development of the method for the refitting the ReaxFF parameters for a system consisting of the mixed transition metal oxides. We have tested several methods allowing to calculate the differences between the vectors of the forces acting on atoms obtained from the reference DFT simulation and the parameters-dependent ReaxFF. We conclude that the footrule method yields the best parameters among the investigated options. We then validate the parameters using the system consisting of the hematite supported (TiO2)n clusters. The results indicate the refitted parameters allow to obtain much better geometries of the clusters upon MD simulation on the ReaxFF level, and despite the short timescale lead to the stable structures.

Performance evaluation of linear discriminant analysis and support vector machines to classify cesarean section

Currently the hospital is a place that is very vulnerable to the transmission of Covid-19, so giving birth in a hospital is very risky. In addition, the hospital currently only accepts cesarean deliveries, while mothers who can give birth vaginally are recommended to give birth in a midwife because the chances of being exposed to Covid-19 are much lower. In general, this study aims to examine the performance of the LDA-SVM method in predicting whether a prospective mother needs to undergo a C-section or simply give birth normally. The aims of this study are: 1) to determine the best parameters for building the detection model; 2) to determine the best accuracy from the model; 3) to compare the accuracies with the other methods. The data used in this study is the dataset of caesarian section. This data consists of the results of 80 pregnant women following C-section with the most important characteristics of labor problems in the clinical field. Based on the results of the experiments that have been carried out, several parameter values that provide the best results for building the detection model are obtained, namely σ (sigma) –5.9 for 70 % training data, σ=4, –6.1 and ‑6.6 for 80 % training data and σ=4 and 16 for 90 % training data. Besides, the results obtained show that the LDA-SVM method is able to classify the C-section method properly with an accuracy of up to 100 %. This research is also able to surpass the methods in previous studies. The results show that LDA-SVM for this case study generates an accuracy of 100.00 %. This method has great potential to be used by doctors used as an early detection to determine whether a mother needs to go through a C-section or simply give birth vaginally. So that mothers can prevent the transmission of Covid-19 in the hospital