Search Results (715)

This paper investigates a back-electromotive force (EMF) harmonic compensation strategy for six-phase permanent-magnet synchronous motors (PMSMs) to reduce current harmonics and improve system performance. Ideally, the back-EMF waveform should be perfectly sinusoidal. However, manufacturing imperfections such as suboptimal magnetic circuit design, uneven winding distribution, and mechanical eccentricity introduce low-order spatial harmonics, particularly the 5th, 7th, 11th, and 13th orders, which distort the back-EMF, increase current harmonics, complicate control, and reduce efficiency. To address these issues, this study proposes a compensation strategy utilizing common-mode and differential-mode current control. By injecting the 6th and 12th harmonics into the decoupled voltage commands along the d-axis and q-axis, the strategy significantly reduces current harmonic distortion. Experimental validation was conducted using a TMS320F28386D microcontroller, which controlled dual inverters via PWM signals and processed real-time current feedback. Rotor position feedback was provided by a resolver to ensure precise and responsive motor control. At a rotational speed of 900 rpm, with a peak phase current $I_m$ of 200 A and an IGBT switching frequency of 10 kHz, the phase-a current total harmonic distortion (THD) was reduced from 11.86% (without compensation) to 6.83% (with compensation). This study focused on mitigating harmonics below the 14th order. The experimental results demonstrate that the proposed back-EMF harmonic compensation strategy effectively minimizes current THD, highlighting its potential for improving the performance and efficiency of multi-phase motor systems. Full article

The use of summarized spectral data in bands obtained by hyperspectral sensors can make it possible to obtain biochemical information about seeds and, thus, relate the results to seed viability and vigor. Thus, the hypothesis of this work is based on the possibility of obtaining information about the physiological quality of seeds through hyperspectral bands and distinguishing seed lots regarding their quality through wavelengths. The objective was then to evaluate the possibility of differentiating soybean genotypes regarding the physiological quality of seeds using spectral data. The experiment was conducted during the 2021/2022 harvest at the Federal University of Mato Grosso do Sul in a randomized block design with four replicates and 10 F3 soybean populations (G1, G8, G12, G15, G19, G21, G24, G27, G31, and G36). After the maturation of each genotype, seeds were harvested from the central rows of each plot, which consisted of five one-meter rows. Seed samples from each experimental unit were placed in a Petri dish to collect spectral data. Readings were performed in the laboratory at a temperature of 26 °C and using two 60 W halogen lamps as the light source, positioned 15 cm between the sensor and the sample. The sensor used was the Ocean Optics (Florida, USA) model STS-VIS-L-50-400-SMA, which captured the reflectance of the seed sample at wavelengths between 450 and 824 nm. After readings from the hyperspectral sensor, the seeds were subjected to tests for water content, germination, first germination count, electrical conductivity, and tetrazolium. The data obtained were subjected to an analysis of variance and the means were compared by the Scott–Knott test at 5% probability, analyzed using R software version 4.2.3 (Auckland, New Zealand). The data on the physiological quality of the seeds of the soybean genotypes were subjected to principal component analysis (PCA) and associated with the K-means algorithm to form groups according to the similarity and distinction between the genetic materials. After the formation of these groups, spectral curve graphs were constructed for each soybean genotype and for the groups that were formed. The physiological quality of the soybean genotypes can be differentiated using hyperspectral bands. The spectral bands, therefore, provide important information about the physiological quality of soybean seeds. Through the use of hyperspectral sensors and the observation of specific bands, it is possible to differentiate genotypes in terms of seed quality, complementing and/or replacing traditional tests in a fast, accurate, and non-destructive way, reducing the time and investment spent on obtaining information on seed viability and vigor. The results found in this study are promising, and further research is needed in future studies with other species and genotypes. The interval between 450 and 649 nm was the main spectrum band that contributed to the differentiation between soybean genotypes of superior and inferior physiological quality. Full article

Temperature stress is one of the major limiting environmental factors that negatively impact global crop yields. Kalanchoë fedtschenkoi is an obligate crassulacean acid metabolism (CAM) plant species, exhibiting much higher water-use efficiency and tolerance to drought and heat stresses than C₃ or C₄ plant species. Previous studies on gene expression responses to low- or high-temperature stress have been focused on C₃ and C₄ plants. There is a lack of information about the regulation of gene expression by low and high temperatures in CAM plants. To address this knowledge gap, we performed transcriptome sequencing (RNA-Seq) of leaf and root tissues of K. fedtschenkoi under cold (8 °C), normal (25 °C), and heat (37 °C) conditions at dawn (i.e., 2 h before the light period) and dusk (i.e., 2 h before the dark period). Our analysis revealed differentially expressed genes (DEGs) under cold or heat treatment in comparison to normal conditions in leaf or root tissue at each of the two time points. In particular, DEGs exhibiting either the same or opposite direction of expression change (either up-regulated or down-regulated) under cold and heat treatments were identified. In addition, we analyzed gene co-expression modules regulated by cold or heat treatment, and we performed in-depth analyses of expression regulation by temperature stresses for selected gene categories, including CAM-related genes, genes encoding heat shock factors and heat shock proteins, circadian rhythm genes, and stomatal movement genes. Our study highlights both the common and distinct molecular strategies employed by CAM and C₃/C₄ plants in adapting to extreme temperatures, providing new insights into the molecular mechanisms underlying temperature stress responses in CAM species. Full article

The present study demonstrated an improvement in both 1-heptene conversion and mono-heptyltoluene selectivity. It simultaneously depicted the isomerization reactions of 1-heptene and toluene alkylation over Y zeolite catalysts having a Si/Al of 3.5 and a surface area of 817 m²/g. The physical properties of the fresh zeolite catalyst were characterized using XRD, FTIR, XRF, TPD, and N₂ adsorption–desorption spectroscopy. The experimental part was carried out in a 100 mL glass flask connected to a reflux condenser at different reaction temperatures ranging from 70 to 90 °C, toluene:1-heptene ratios of 3–8, and catalyst weights of 0.25–0.4 g. The highest conversion of ~96% was obtained at the highest toluene:1-heptene ratio (i.e., 8:1), 0.25 g of zeolite Y, at 180 min of reaction time and under a reaction temperature of 90 °C. However, the selectivity of 2-heptyltoluene reached its highest value of ~25% under these conditions. Likewise, the kinetic modeling developed in this study helped describe the proposed reaction mechanism by linking the experimental results with the predicted results. The kinetic parameters were determined by nonlinear regression analysis using the MATLAB^® package genetic algorithm. The ordinary differential equations were integrated with respect to time using the fourth-order Runge–Kutta method, and the resulting mole fractions were fitted against the experimental data. The mean relative error (MRE) values were calculated from the experimental and predicted results, which showed a reasonable agreement with the average MRE being ~11.7%. The calculated activation energies showed that the reaction rate follows the following order: coking (55.9–362.7 kJ/mol) > alkylation (73.1–332.1 kJ/mol) > isomerization (69.3–120.2 kJ/mol), indicating that isomerization reactions are the fastest compared to other reactions. A residual activity deactivation model was developed to measure the deactivation kinetic parameters, and the deactivation energy value obtained was about 48.2 kJ/mol. Full article

Magnaporthe oryzae causes devastating rice blast disease, significantly impacting rice production in many countries. Among the many known resistance (R) genes, Piz-t confers broad-spectrum resistance to M. oryzae isolates and encodes a nucleotide-binding site leucine-rich repeat receptor (NLR). Although Piz-t-interacting proteins and those in the signal transduction pathway have been identified over the last decade, the Piz-t-mediated resistance has not been fully understood at the transcriptomic and metabolomic levels. In this study, we performed transcriptomic and metabolomic analyses in the Piz-t plants after inoculation with M. oryzae. The transcriptomic analysis identified a total of 15,571 differentially expressed genes (DEGs) from infected Piz-t and wild-type plants, with 2791 being Piz-t-specific. K-means clustering, GO term analysis, and KEGG enrichment pathway analyses of the total DEGs identified five groups of DEGs with distinct gene expression patterns at different time points post inoculation. GO term analysis of the 2791 Piz-t-specific DEGs revealed that pathways related to DNA organization, gene expression regulation, and cell division were highly enriched in the group, especially at early infection stages. The gene expression patterns in the transcriptomic datasets were well correlated with the metabolomic profiling. Broad-spectrum “pathway-level” metabolomic analyses indicated that terpenoid, phenylpropanoid, flavonoid, fatty acid, amino acid, glycolysis/TCA, and phenylalanine pathways were altered in the Piz-t plants after M. oryzae infection. This study offers new insights into the molecular dynamics of transcripts and metabolites in R-gene-mediated resistance against M. oryzae and provides candidates for enhancing rice blast resistance through the engineering of metabolic pathways. Full article

Background: Quantitative pupillometry has been proposed as an objective means to diagnose acute sports-related concussion (SRC). Objective: To assess the diagnostic accuracy of a smartphone-based quantitative pupillometer in the acute diagnosis of SRC. Methods: Division I college football players had baseline pupillometry including pupillary light reflex (PLR) parameters of maximum resting diameter, minimum diameter after light stimulus, percent change in pupil diameter, latency of pupil constriction onset, mean constriction velocity, maximum constriction velocity, and mean dilation velocity using a smartphone-based app. When an SRC occurred, athletes had the smartphone pupillometry repeated as part of their concussion testing. All combinations of the seven PLR parameters were tested in machine learning binary classification models to determine the optimal combination for differentiating between non-concussed and concussed athletes. Results: 93 football athletes underwent baseline pupillometry testing. Among these athletes, 11 suffered future SRC and had pupillometry recordings repeated at the time of diagnosis. In the machine learning pupillometry analysis that used the synthetic minority oversampling technique to account for the significant class imbalance in our dataset, the best-performing model was a random forest algorithm with the combination of latency, maximum diameter, minimum diameter, mean constriction velocity, and maximum constriction velocity PLR parameters as feature inputs. This model produced 91% overall accuracy, 98% sensitivity, 84.2% specificity, area under the curve (AUC) of 0.91, and an F1 score of 91.6% in differentiating between baseline and SRC recordings. In the machine learning analysis prior to oversampling of our imbalanced dataset, the best-performing model was k-nearest neighbors using latency, maximum diameter, maximum constriction velocity, and mean dilation velocity to produce 82% accuracy, 40% sensitivity, 87% specificity, AUC of 0.64, and F1 score of 24%. Conclusions: Smartphone pupillometry in combination with machine learning may provide fast and objective SRC diagnosis in football athletes. Full article

The basic helix-loop-helix (bHLH) transcription factor (TF) family, the second-largest among eukaryotes, is known for its evolutionary and functional diversity across plant species. However, bHLH genes have not yet been characterized in Areca catechu. In this study, we identified 76 AcbHLH genes, which exhibit a variety of physicochemical properties. Phylogenetic analysis revealed evolutionary relationships between Arabidopsis thaliana bHLH genes (AtbHLH) and their counterparts in A. catechu (AcbHLH). These analyses also highlighted conserved amino acid motifs (S, R, K, P, L, A, G, and D), conserved domains, and evolutionary changes, such as insertions, deletions, and exon gains or losses. Promoter analysis of AcbHLH genes revealed 76 cis-elements related to growth, phytohormones, light, and stress. Gene duplication analysis revealed four tandem duplications and twenty-three segmental duplications, while AcbHLH63 in the Areca genome exhibited significant synteny with bHLH genes from A. thaliana, Vitis vinifera, Solanum lycopersicum, Brachypodium distachyon, Oryza sativa, and Zea mays. Furthermore, relative expression analysis showed that under drought stress (DS), AcbHLH22, AcbHLH39, AcbHLH45, and AcbHLH58 showed distinct upregulation in leaves at specific time points, while all nine AcbHLH genes were upregulated in roots. Under salt stress (SS), AcbHLH22, AcbHLH39, AcbHLH45, and AcbHLH58 were upregulated in leaves, and AcbHLH22, AcbHLH34, and AcbHLH39 exhibited differential expression in roots at various time points. This study provides valuable insights into the bHLH superfamily in A. catechu, offering a solid foundation for further investigation into its role in responding to abiotic stresses. Full article

In recent decades, there have been many studies on Hopf bifurcation and population stability with time delay. However, the stability and Hopf bifurcation of fractional-order population systems with time delay are lower. In this paper, we discuss the dynamic behavior of a fractional-order three-population model with pregnancy delay using Laplace transform of fractional differential equations, stability and bifurcation theory, and MATLAB software. The specific conditions of local asymptotic stability and Hopf bifurcation for fractional-order time-delay systems are determined. A fractional-order proportional–integral–derivative (PID) controller is applied to the three-population food chain system for the first time. The convergent speed and vibration amplitude of the system can be changed by PID control. For example, after fixing the values of the integral control gain $k_i$ and the differential control gain $k_d$, the amplitude of the system decreases and the convergence speed changes as the proportional control gain $k_p$ decreases. The effectiveness of the PID control strategy in complex ecosystem is proved. The numerical simulation results are in good agreement with the theoretical analysis. The research in this paper has potential application values concerning the management of complex population systems. The bifurcation theory of fractional-order time-delay systems is also enriched. Full article

Background: Oncostatin M (OSM) is involved in several inflammatory responses. Tryptanthrin (TRYP), as a natural alkaloid, is a bioactive compound derived from indigo plants. Objectives/ Methods: The purpose of this study is to investigate the potential inhibitory activity of TRYP on OSM release from neutrophils using neutrophils-like differentiated (d)HL-60 cells and neutrophils from mouse bone marrow. Results: The results showed that TRYP reduced the production and mRNA expression levels of OSM in the granulocyte–macrophage colony-stimulating factor (GM-CSF)-stimulated neutrophils-like dHL-60 cells. In addition, TRYP decreased the OSM production levels in the GM-CSF-stimulated neutrophils from mouse bone marrow. TRYP inhibited the phosphorylation of phosphatidylinositol 3-kinase (PI3K), AKT, and nuclear factor (NF)-κB in the GM-CSF-stimulated neutrophils-like dHL-60 cells. Conclusions: Therefore, these results reveal for the first time that TRYP inhibits OSM release via the down-regulation of PI3K-AKT-NF-κB axis from neutrophils, presenting its potential as a therapeutic agent for inflammatory responses. Full article

This study presents a synthesizer topology based on a delay-locked loop (DLL) and programmable frequency multiplier for 5G⁺ communication systems. The proposed synthesizer comprises a 512-phase DLL, an intermediate frequency generator (IFG), and an RF frequency multiplier (RFFM). The 512-phase DLL provides 512 delayed pulses through a chain of 256 delay units and single-to-differential complementary converters (S2DCs). The IFG comprises I/Q-multiplexers, I/Q-accumulators, an XOR, and an S2DC. The I/Q-multiplexer outputs switch to the phase lag or lead waveforms at every rising or falling edge of the outputs, which makes the I/Q-multiplexer output frequency, f_MX, programmable. The IF, f_IF, is two times f_MX, and f_IF is up-converted to RF, f_RF, through the RFFM. When the reference clock frequency, f_ref, is 156.25 MHz, the f_IF range is 156.863–312.5 MHz and the f_RF dynamic range is approximately 1.89–9.96 GHz. The channel resolution range is 3.698–38.609 MHz. Consequently, the proposed synthesizer provides a wide 134% output frequency bandwidth and a finer channel resolution smaller than f_ref. The presented synthesizer is fabricated in a 65 nm CMOS process. The total power consumption is 15 mW, and the rms jitter integrated from 1 kHz to 100 MHz measured as 107.6 fs. Full article

The goal of this study is to develop numerous Hermite–Hadamard–Mercer (H–H–M)-type inequalities involving various fractional integral operators, including classical, Riemann–Liouville (R.L), k-Riemann–Liouville (k-R.L), and their generalized fractional integral operators. In addition, we establish a number of corresponding fractional integral inequalities for three-times differentiable convex functions that are connected to the right side of the H–H–M-type inequality. For these results, further remarks and observations are provided. Following that, a couple of graphical representations are shown to highlight the key findings of our study. Finally, some applications on special means are shown to demonstrate the effectiveness of our inequalities. Full article

High-resolution images captured by Unmanned Aerial Vehicles (UAVs) play a vital role in precision agriculture, particularly in evaluating crop health and detecting weeds. However, the detailed pixel information in these images makes classification a time-consuming and resource-intensive process. Despite these challenges, UAV imagery is increasingly utilized for various agricultural classification tasks. This study introduces an automatic classification method designed to streamline the process, specifically targeting cassava plants, weeds, and soil classification. The approach combines K-means unsupervised classification with spectral trend-based labeling, significantly reducing the need for manual intervention. The method ensures reliable and accurate classification results by leveraging color indices derived from RGB data and applying mean-shift filtering parameters. Key findings reveal that the combination of the blue (B) channel, Visible Atmospherically Resistant Index (VARI), and color index (CI) with filtering parameters, including a spatial radius (sp) = 5 and a color radius (sr) = 10, effectively differentiates soil from vegetation. Notably, using the green (G) channel, excess red (ExR), and excess green (ExG) with filtering parameters (sp = 10, sr = 20) successfully distinguishes cassava from weeds. The classification maps generated by this method achieved high kappa coefficients of 0.96, with accuracy levels comparable to supervised methods like Random Forest classification. This technique offers significant reductions in processing time compared to traditional methods and does not require training data, making it adaptable to different cassava fields captured by various UAV-mounted optical sensors. Ultimately, the proposed classification process minimizes manual intervention by incorporating efficient pre-processing steps into the classification workflow, making it a valuable tool for precision agriculture. Full article

Background: Cultivated meat, an alternative to conventional meat, has substantial potential for alleviating environmental and ethical concerns. This method of manufacturing meat involves the isolation of skeletal muscle satellite cells (SMSCs) from donor animals, after which they proliferate in vitro and differentiate into primitive muscle fibers. The aim of this research was to evaluate how the insulin-like growth factor 1 (IGF1) gene regulates the myogenic differentiation of bovine skeletal muscle satellite cells (bSMSCs). Methods: bSMSCs isolated from newborn calves were cultured to the third generation in vitro and differentiated into myoblasts via the serum withdrawal method. An overexpression lentivirus and siRNA targeting the IGF1 gene were constructed and transduced into bSMSCs, which were subsequently analyzed via real-time fluorescence quantitative PCR(qRT–PCR) and Western blots. The mRNA and protein levels of the myogenic differentiation markers myosin heavy chain (MyHC) and myogenin (MyoG) were determined. Results: The results revealed that the lentivirus overexpressing the IGF1 gene significantly increased the expression of MyHC and MyoG, whereas the expression of both the MyHC and MyoG mRNAs and proteins was strongly reduced by si-IGF1. Conclusions: IGF1 positively regulates the myogenic differentiation of bSMSCs. This study provides a reference for further elucidating the molecular mechanism by which the IGF1 gene regulates the myogenic differentiation of bSMSCs via the PI3K/Akt signaling pathway and lays a foundation for establishing a regulatory network of bovine muscle growth and development. Full article

The solid phase byproduct obtained after conventional fucoidan extraction from the brown seaweed Fucus vesiculosus can be used as a source containing alginate. This study involves ultrasound-assisted extraction (UAE) of alginate from the byproduct using sodium bicarbonate. Response surface methodology (RSM) was applied to obtain the optimum conditions for alginate extraction. The ultrasound (US) treatments included 20 kHz of frequency, 20–91% of amplitude, and an extraction time of 6–34 min. The studied investigated the crude alginate yield (%), molecular weight, and alginate content (%) of the extracts. The optimum conditions for obtaining alginate with low molecular weight were found to be 69% US amplitude and sonication time of 30 min. The alginate extracts obtained were characterized using Fourier transform infrared (FTIR) spectroscopy, thermogravimetric analysis (TGA), and differential scanning calorimetry (DSC). Ultrasound-assisted extraction involving a short treatment lasting 6–34 min was found to be effective in extracting alginate from the byproduct compared to the conventional extraction of alginate using stirring at 415 rpm and 60 °C for 24 h. The US treatments did not adversely impact the alginate obtained, and the extracted alginates were found to have similar characteristics to the alginate obtained from conventional extraction and commercial sodium alginate. Full article

Radiomics provides a structured approach to support clinical decision-making through key steps; however, users often face difficulties when switching between various software platforms to complete the workflow. To streamline this process, matRadiomics integrates the entire radiomics workflow within a single platform. This study extends matRadiomics to preclinical settings and validates it through a case study focused on early malformation differentiation in a zebrafish model. The proposed plugin incorporates Pyradiomics and streamlines feature extraction, selection, and classification using machine learning models (linear discriminant analysis—LDA; k-nearest neighbors—KNNs; and support vector machines—SVMs) with k-fold cross-validation for model validation. Classifier performances are evaluated using area under the ROC curve (AUC) and accuracy. The case study indicated the criticality of the long time required to extract features from preclinical images, generally of higher resolution than clinical images. To address this, a feature analysis was conducted to optimize settings, reducing extraction time while maintaining similarity to the original features. As a result, SVM exhibited the best performance for early malformation differentiation in zebrafish (AUC = 0.723; accuracy of 0.72). This case study underscores the plugin’s versatility and effectiveness in early biological outcome prediction, emphasizing its applicability across biomedical research fields. Full article