Search Results (1,031)

In this paper, an iterative learning control without resetting conditions based on a finite-time zeroing neural network (NRCILC-FTZNN) is designed for trajectory tracking of a robotic manipulator operating under external disturbances and executing repetitive tasks. A finite-time zeroing neural network (FTZNN) is developed to eliminate external disturbances and enhance convergence. Furthermore, an iterative learning control without resetting conditions based on the FTZNN is proposed to automatically provide the initial state value in each iteration, thereby eliminating the need for reset conditions. The trajectory-tracking errors, measured by the mean absolute error (MAE), are reduced by 46.89% and 63.29% compared to other schemes. Furthermore, the tracking errors of the proposed NRCILC-FTZNN method converge to zero in fewer iterations than those of the other methods. Simulation results demonstrate the convergence of the robotic manipulator system under disturbances to confirm the effectiveness of NRCILC-FTZNN scheme. Full article

Traditional medicinal plants are valued for their therapeutic potential, yet the full spectrum of their bioactive compounds often remains underexplored. Recent advances in multiomics technologies, including metabolomics, proteomics, and transcriptomics, combined with in vitro culture systems and elicitor-based strategies, have revolutionized our ability to characterize and enhance the production of valuable secondary metabolites. This review synthesizes current findings on the integration of these approaches to help us understand phytochemical pathways optimising bioactive compound yields. We explore how metabolomic profiling links chemical diversity with antioxidant and antimicrobial activities, how proteomic insights reveal regulatory mechanisms activated during elicitation, and how in vitro systems enable controlled manipulation of metabolic outputs. Both biotic and abiotic elicitors, such as methyl jasmonate and salicylic acid, are discussed as key triggers of phytochemical defense pathways. Further, we examine the potential of multiomics-informed metabolic engineering and synthetic biology to scale production and discover novel compounds. By aligning traditional ethnobotanical knowledge with modern biotechnology, this integrative framework offers a powerful avenue to unlock the pharmacological potential of medicinal plants for sustainable and innovative therapeutic development. Full article

The stable control of the main steam flow rate (MSFR) can effectively improve the waste combustion efficiency and energy utilization, reduce environmental pollution, and is crucial for promoting the sustainable development of municipal solid waste incineration (MSWI). Developed countries benefit from stable municipal solid waste (MSW) composition, enabling advanced automated combustion control. However, in developing countries, fluctuating waste composition and calorific value cause frequent disturbances, limiting the use of foreign control methods. Therefore, MSFR control technologies suited to developing countries are crucial. This study proposes a two-layer intelligent control method, consisting of an optimization setting layer and a loop control layer. The optimization layer uses a steam flow prediction model (OPTICS and RBF) and an improved antlion optimizer (IALO) for manipulated variable setpoints. The control layer applies reinforcement learning (actor–critic) to fine-tune PI controller parameters. Experimental results show that the proposed method adaptively adjusts manipulated variables, ensuring MSFR control within the target range and maintaining efficient, stable MSWI operation. Full article

Flexible manipulators are widely applied in many fields. Here, the control design for a simplified flexible manipulator model with nonlinear inputs and state constraints is studied. The impact of two inputs and disturbances on the system was considered. One torque input comes from the joint motor, and the other input force comes from the linkage actuator tip. The input constraints of a dead zone are applied to both inputs to the manipulator. To offset the effect of the nonlinear input, we first linearize the dead zone and convert it into a linear-input characteristic and a finite error value. Then, the adaptive rate is designed to compensate for the effects of the nonlinear input. For the state constraints, an adaptive controller is proposed based on a symmetric tangent-type barrier Lyapunov function which can operate under closer constraint conditions, and parameter tunability offers flexibility in balancing the constraints’ tightness and performance. The stability proof ensures that all states are within the given constraint range. The provided simulation results indicate that the system is not sensitive to the initial values, and when the initial values are taken to be between open intervals (−0.4, 0.34), this ensures the stability of the system and does not violate the constraint bounds. Full article

The rapid growth of the Internet of Things (IoT) has revolutionized various industries by enabling interconnected devices to exchange data seamlessly. However, IoT systems face significant security challenges due to decentralized architectures, resource-constrained devices, and dynamic network environments. These challenges include denial-of-service (DoS) attacks, anomalous network behaviors, and data manipulation, which threaten the security and reliability of IoT ecosystems. New methods based on machine learning have been reported in the literature, addressing topics such as intrusion detection and prevention. This paper proposes an advanced anomaly detection framework for IoT networks expressed in several phases. In the first phase, data preprocessing is conducted using techniques like the Median-KS Test to remove noise, handle missing values, and balance datasets, ensuring a clean and structured input for subsequent phases. The second phase focuses on optimal feature selection using a Genetic Algorithm enhanced with eagle-inspired search strategies. This approach identifies the most significant features, reduces dimensionality, and enhances computational efficiency without sacrificing accuracy. In the final phase, an ensemble classifier combines the strengths of the Decision Tree, Random Forest, and XGBoost algorithms to achieve the accurate and robust detection of anomalous behaviors. This multi-step methodology ensures adaptability and scalability in handling diverse IoT scenarios. The evaluation results demonstrate the superiority of the proposed framework over existing methods. It achieves a 12.5% improvement in accuracy (98%), a 14% increase in detection rate (95%), a 9.3% reduction in false positive rate (10%), and a 10.8% decrease in false negative rate (5%). These results underscore the framework’s effectiveness, reliability, and scalability for securing real-world IoT networks against evolving cyber threats. Full article

Magnetic levitation enables the confinement and melting of conductive metals using alternating magnetic fields, eliminating the need for a crucible or other contact supports. This makes the technology particularly suitable for applications where container use is impractical, such as preventing contamination between the melt and the crucible, handling high-purity materials, or facilitating in-orbit operations. For a given coil design and load, selecting the appropriate feeding parameters, such as the current and frequency, is crucial to ensure the correct operation of the device. This study investigates the optimal current and frequency values required to levitate an aluminum billet using a proposed longitudinal electromagnetic levitator, which represents an initial prototype of a more complex system for automated material manipulation. The analysis was conducted through 2D and 3D finite element method (FEM) simulations, assessing the equilibrium position and stability with respect to translations and rotations under various operating conditions. The study identifies an operating configuration that ensures vertical stability while minimizing excessive heating, in order to obtain a sufficiently long confinement time before the melting point is reached. A fully coupled 2D thermal simulation was then performed to assess the billet’s heating rate under the selected operating conditions. Finally, an experiment was conducted on a prototype to confirm billet levitation. Full article

Although numerous rice plant height-related genes have been cloned and functionally characterized in recent years, a gap between the identified genes and their utilization in breeding still exists. Here, we developed a linear regression model by pyramiding plant height-related alleles to predict rice plant height and confirmed that it can be used in rice breeding. In our study, we firstly identified 22 plant height-associated molecular markers from 218 markers in an association mapping population which consisted of 273 rice varieties. Linear regression analysis revealed a positive correlation between rice plant height and the number of plant height-increasing alleles derived from these 22 molecular markers. Subsequently, linear regression models were developed using 2–10 loci based on the genotype and phenotype data of the association mapping population. The predictive accuracy of the model was tested using a recombinant inbred line (RIL) population consisting of 219 lines, and it revealed the trend that predictive accuracy increased with more loci in a certain range of less than five loci. If the prediction model was built based on 5–10 loci, it yielded an average absolute error from 11.05 to 11.96 cm, which was smaller than absolute error induced by environmental factors (5.72 cm to 12.79 cm). The reliable prediction of rice plant height by this model highlights its value as a practical tool for optimizing rice breeding strategies. Additionally, the linear regression model developed in this study not only can facilitate plant height manipulation but also will inspire other design breeding techniques in other crops or other traits. Full article

Nickel (Ni) ultrathin films exhibit phase-dependent electrical, magnetic, and optical characteristics that are significantly influenced by deposition methods. However, these films are inherently prone to rapid oxidation, with the oxidation rate dependent on substrate, temperature, and deposition parameters. The focus of this research is to investigate the temporal oxidation of RF-sputtered Ni ultrathin films on Corning glass under ambient atmospheric conditions and its impact on their structural, surface, and optical characteristics. Controlled film thicknesses were achieved through precise manipulation of deposition parameters, enabling the analysis of oxidation-induced modifications. Atomic force microscopy (AFM) revealed that films with high structural integrity and surface uniformity are exhibiting roughness values (Rq) from 0.679 to 4.379 nm of corresponding thicknesses ranging from 4 to 85 nm. Scanning electron microscopy (SEM) validated the formation of Ni grains interspersed with NiO phases, facilitating SPR-like effects. UV-visible spectroscopy is demonstrating thickness-dependent spectral (plasmonic peak) shifts. Finite Difference Time Domain (FDTD) simulations corroborate the observed thickness-dependent optical absorbance and the resultant shifts in the absorbance-induced plasmonic peak position and bandgap. Increased NiO presence primarily drives the enhancement of electromagnetic (EM) field localization and the direct impact on power absorption efficiency, which are modulated by the tunability of the plasmonic peak position. Our work demonstrates that controlled fabrication conditions and optimal film thickness selection allow for accurate manipulation of the Ni oxidation process, significantly altering their optical properties. This enables the tailoring of these Ni films for applications in transparent conductive electrodes (TCEs), magneto-optic (MO) devices, spintronics, wear-resistant coatings, microelectronics, and photonics. Full article

Free-Space Optical (FSO) communication systems operating in arid regions, especially those envisioned for current and future 5G/6G networks, are significantly affected by dust storms, which cause signal attenuation and service disruptions. While previous studies have proposed deterministic models to characterize attenuation in both controlled and real environments, probabilistic modeling approaches remain largely unexplored, particularly for capturing the variability of FSO signal attenuation under dust conditions. This study proposes a probabilistic model for FSO signal attenuation developed from experiments conducted in a repeatable and well-characterized controlled dust chamber. The chamber-based setup allowed precise manipulation of dust visibility levels and consistent data collection, serving as a benchmark for statistical modeling. We analyzed the measurements to fit appropriate probability distributions for modeling the signal attenuation as a random variable. The empirical data were fitted to several candidate distributions, and the Johnson SB distribution consistently achieved superior performance with $R^2\ge 0.95$ and RMSE and MAE values close to zero across all dust conditions. The results offer a foundational framework for modeling dust-induced attenuation as a random process, providing statistical bounds for FSO link planning in desert environments. Full article

Background: There are a few very specific inflammation biomarkers in blood, namely lipoprotein NMe⁺ signals of protein clusters (GlycA and GlycB) and a composite resonance of phospholipids (SPC). The relative integrals of these resonances provide clear indication of the unique metabolic changes associated with disease, specifically inflammatory conditions, often related to serious diseases such as cancer or COVID-19 infection. Relatively complicated, yet very efficient experimental methods have been introduced recently (DIRE, JEDI) to suppress the rest of the spectrum, thus allowing measurement of these integrals of interest. Methods: In this study, we introduce a simple alternative processing method using CRAFT (Complete Reduction to Amplitude-Frequency Table), a time-domain (FID) analysis tool which can highlight selected subsets of the spectrum by choice for quantitative analysis. The output of this approach is a direct, spreadsheet-based representation of the required peak amplitude (integral) values, ready for comparative analysis, completely avoiding all the convectional data processing and manipulation steps. The significant advantage of this alternative method is that it only needs a simple water-suppressed 1D spectrum with no further experimental manipulation whatsoever. In addition, there are no pre/post processing steps (such as baseline and/or phase), further minimizing potential dependency on subjective decisions by the user and providing an opportunity to automate the entire process. Results: We applied this methodology to horse serum samples to follow the presence of inflammation for cohorts with or without OCD (Osteochondritis Dissecans) conditions and find diagnostic separation of the of the cohorts through statistical methods. Conclusions: The powerful and simple CRAFT-based approach is suitable to extract selected biomarker information from complex NMR spectra and can be similarly applied to any other biofluid from any source or sample, also retrospectively. There is a potential to extend such a simple analysis to other, previously identified relevant markers as well. Full article

Machining efficiency and stability are crucial for achieving high-quality manufacturing outcomes. One of the primary challenges in machining is the suppression of chatter, which negatively impacts surface finish, tool longevity, and overall process reliability. This study proposes a machine learning-based approach to optimize feed rate in machining operations by integrating a genetic algorithm (GA)-calibrated cutting force model with vibration analysis. A theoretical cutting force dataset is generated under varying machining conditions, followed by frequency-domain analysis using Fast Fourier Transform (FFT) to identify feed rates that minimize chatter. These optimal feed rates are then used to train an Extreme Gradient Boosting (XGBoost) regression model, with Bayesian optimization employed for hyperparameter tuning. The trained model achieves an R² score of 0.7887, indicating strong prediction accuracy. To verify the model’s effectiveness, robotic milling experiments were conducted using a UR10e manipulator. Surface quality evaluations showed that the model-predicted feed rates consistently resulted in better surface finish and reduced chatter effects compared to conventional settings. These findings validate the model’s ability to enhance machining performance and demonstrate the practical value of integrating simulated dynamics and machine learning for data-driven parameter optimization in robotic systems. Full article

The Airyprime beam, due to its adjustable focusing ability and controllable orbital angular momentum, has attracted significant attention in fields such as free-space optical communication and particle trapping. However, systematic studies on the propagation behavior of oscillating solitons in PT-symmetric optical lattices remain scarce, particularly regarding their formation mechanisms and self-accelerating characteristics. In this study, the propagation characteristics of Airyprime beams in PT symmetric optical lattices are numerically studied using the split-step Fourier method, and the generation mechanism and control factors of oscillating solitons are analyzed. The influence of lattice parameters (such as the modulation depth P, modulation frequency w, and gain/loss distribution coefficient W₀) and beam initial characteristics (such as the truncation coefficient a) on the dynamic behavior of the beam is revealed. The results show that the initial parameters determine the propagation characteristics of the beam and the stability of the soliton. This research provides theoretical support for beam shaping, optical path design, and nonlinear optical manipulation and has important application value. Full article

Concealing images has been a concern of artists and scientists, as have the conditions that can reveal them. It is relatively easy to hide images in pictures, but this is of little value if they remain hidden. The skill is in revealing previously concealed images. Three aspects of hiding images are examined, two of which are monocular and the third is binocular. Firstly, high-contrast patterns, like Street figures and Mooney faces, have been used in psychological tests of pattern recognition, and Gestalt grouping principles can result in concealing images. Second, it is possible to hide low spatial frequency content carried by high-spatial-frequency patterns. A wider range of carriers than gratings can be used, like graphics, photographs, and combinations of them (photo-graphics). Pictorial images can be concealed in terms of detection or recognition. In both cases, there is interplay between the global features of the concealed image and the local elements that carry it. Third, randomly textured stereograms reveal to two eyes what is concealed from each one alone—stereoscopic depth. The dimension of stereoscopic depth can be manipulated, as can that of binocular rivalry, to conceal images. Full article

In this paper, a global amplitude-controlled discrete hyperchaotic memristive map is designed utilizing the hyperbolic tangent function. This map exhibits fixed points arranged in a line along the y-axis, and the stability distributions of these fixed points are delineated based on variations in both the initial conditions of the map and the parameter plane. The dynamic characteristics of the map were examined through the analysis of its 2D dynamics and the largest Lyapunov exponent (LE) distribution. The existence of multistability was robustly confirmed through a comprehensive analysis of the basin of attraction, the spectra of LE that depend on initial values, bifurcation diagrams, and trajectory plots. Additionally, the amplitude of the map can be adjusted both globally and locally through manipulation of the non-bifurcation parameter. Subsequently, a digital circuit powered by a microcontroller was designed to embody the map. In comparison to recent maps, the newly devised map exhibits superior efficacy in the realm of image encryption applications. Full article

As the global demand for sustainable protein sources grows, valorizing side streams in plant protein processing has become crucial. This study revisits the conventional alkaline–isoelectric extraction of faba bean protein isolates, introducing an enhanced mass balance-driven approach to recover underutilized protein fractions from typically discarded side streams. Through strategic pH manipulation and centrifugation, four distinct protein fractions were recovered with purities ranging from 34.6% to 89.6%, collectively recapturing a significant portion of the 16% protein loss in standard processing. SDS-PAGE and FTIR analyses confirmed the structural diversity among the recovered fractions, with albumin-rich and globulin-rich profiles exhibiting unique spectral and electrophoretic signatures. Functionally, fractions B and D exhibited superior water- and oil-holding capacities, indicating their potential utility in food formulations requiring enhanced moisture and lipid retention. In contrast, fraction C, characterized by low water-holding capacity and high solubility, may be better suited to applications prioritizing emulsification performance, such as in dairy or meat analogs. This study not only highlights the feasibility of reclaiming high-quality protein from industrial byproducts but also underscores the potential of these recovered proteins in diverse food and non-food sectors, including pharmaceuticals and cosmetics. These findings contribute to circular economy strategies by transforming waste into value-added ingredients with functional and commercial significance. Full article