Search Results (43)

There is a complex dynamic interaction between the aero-engine bearing and the rotor, and the resulting time-varying system parameters have an impact on the nonlinear dynamic characteristics of the rolling bearing-flexible rotor system. In this study, the interaction between the time-varying stiffness of the rolling bearing and the transient response of the flexible rotor is considered. The Newmark-β integral method is used to solve the dynamic equation, and the relationship between the time-varying characteristics of bearing stiffness and load and the dynamic characteristics of the rotor is studied. The relationship between bearing stiffness and vibration strength is analyzed, and the influence of damage size on the time domain signal energy of the disc is analyzed. The results show that the model established in this paper can accurately reflect the dynamic interaction between the bearing and the rotor. With the extension of the bearing damage, the dynamic stiffness of the bearing attenuates, the intensity of the excitation force increases, and the vibration is transmitted to the disc, which affects the motion stability and vibration response of the disc. Full article

Non-immunoglobulin-based scaffold proteins (SPs) represent one of the key therapeutic target-specific and high-affinity binders in modern medicine. Among their cellular targets are signaling receptors, in particular, receptor tyrosine kinases, whose dysfunction leads to the development of cancer and other serious diseases. Successful applications of SPs have been reported for HER receptor type 2 (HER2), a member of the human epidermal growth factor receptor family that regulates cell growth and differentiation. To extend the blood residence of SPs and prevent their high accumulation in the kidneys, these proteins are often fused with serum albumin. Promising results for HER2-binding activity were obtained for SP G3 from the DARPins (Designed Ankyrin Repeat Proteins) family fused with an albumin-binding domain (ABD). Interestingly, the detected HER2–G3 binding strongly depended on the position of the G3 module in the sequence of the constructs. Further improvement of these constructs for biomedical applications requires deciphering the molecular mechanism responsible for this effect. Here, we investigate the structural and dynamic aspects of ABD–G3 and G3–ABD chimeras using NMR spectroscopy and molecular modeling. Based on biophysical data, we come to the conclusion that extensive inter-domain contacts form in both constructs, although their binding interfaces and complex stability are somewhat different. Also, it is shown that the domain linker plays an important role—it limits the accessibility of the detected protein–protein binding sites, depending on the order of the domains in the chimeric molecules. These results create a solid structural basis for the rational design of new effective SP constructs targeting the signaling receptors in cells. Full article

This paper investigates the problem of observer-based control for a class of nonlinear systems described by the Caputo–Hadamard fractional-order derivative. Given the growing interest in fractional-order systems for their ability to capture complex dynamics, ensuring their practical stability remains a significant challenge. We propose a novel concept of practical stability tailored to nonlinear Hadamard fractional-order systems, which guarantees that the system solutions converge to a small ball containing the origin, thereby enhancing their robustness against perturbations. Furthermore, we introduce a practical observer design that extends the classical observer framework to fractional-order systems under an enhanced One-Sided Lipschitz (OSL) condition. This extended OSL condition ensures the convergence of the proposed practical observer, even in the presence of significant nonlinearities and disturbances. Notably, the novelty of our approach lies in the extension of both the practical observer and the stability criteria, which are innovative even in the integer-order case. Theoretical results are substantiated through numerical examples, demonstrating the feasibility of the proposed method in real-world control applications. Our contributions pave the way for the development of robust observers in fractional-order systems, with potential applications across various engineering domains. Full article

Control technology for wheeled mobile robots is one of the core focuses in the current field of robotics research. Within this domain, model-free adaptive control (MFAC) methods, with their advanced data-driven strategies, have garnered widespread attention. The unique characteristic of these methods is their ability to operate without relying on prior model information of the control system, which showcases their exceptional capability in ensuring closed-loop system stability. This paper extensively details three dynamic linearization techniques of MFAC: compact form dynamic linearization, partial form dynamic linearization and full form dynamic linearization. These techniques lay a solid theoretical foundation for MFAC. Subsequently, the article delves into some advanced MFAC schemes, such as dynamic event-triggered MFAC and iterative learning MFAC. These schemes further enhance the efficiency and intelligence level of control systems. In the concluding section, the paper briefly discusses the future development potential and possible research directions of MFAC, aiming to offer references and insights for future innovations in control technology for wheeled mobile robots. Full article

The fruit surface is a critical first line of defense against environmental stress. Overlaying the fruit epidermis is the cuticle, comprising a matrix of cutin monomers and waxes that provides protection and mechanical support throughout development. The epidermal layer of the cucumber (Cucumis sativus L.) fruit also contains prominent lipid droplets, which have recently been recognized as dynamic organelles involved in lipid storage and metabolism, stress response, and the accumulation of specialized metabolites. Our objective was to genetically characterize natural variations for traits associated with the cuticle and lipid droplets in cucumber fruit. Phenotypic characterization and genome-wide association studies (GWAS) were performed using a resequenced cucumber core collection accounting for >96% of the allelic diversity present in the U.S. National Plant Germplasm System collection. The collection was grown in the field, and fruit were harvested at 16–20 days post-anthesis, an age when the cuticle thickness and the number and size of lipid droplets have stabilized. Fresh fruit tissue sections were prepared to measure cuticle thickness and lipid droplet size and number. The collection showed extensive variation for the measured traits. GWAS identified several QTLs corresponding with genes previously implicated in cuticle or lipid biosynthesis, including the transcription factor SHINE1/WIN1, as well as suggesting new candidate genes, including a potential lipid-transfer domain containing protein found in association with isolated lipid droplets. Full article

Background: Alzheimer’s disease is a serious and widespread neurodegenerative illness in the modern healthcare scenario. GSK-3β and BuChE are prominent enzymatic targets associated with Alzheimer’s disease. Co-targeting GSK3β and BChE in Alzheimer’s disease helps to modify disease progression and enhance cognitive function by addressing both tau pathology and cholinergic deficits. However, the treatment arsenal for Alzheimer’s disease is extremely inadequate, with present medications displaying dismal success in treating this never-ending ailment. To create novel dual inhibitors, we have used molecular docking and dynamics analysis. Our focus was on analogs formed from the fusion of tacrine and amantadine ureido, specifically tailored to target GSK-3β and BuChE. Methods: In the following study, molecular docking was executed by employing AutoDock Vina and molecular dynamics and ADMET predictions were performed using the Desmond and Qikprop modules of Schrödinger. Results: Our findings unveiled that compounds DKS1 and DKS4 exhibited extraordinary molecular interactions within the active domains of GSK-3β and BuChE, respectively. These compounds engaged in highly favorable interactions with critical amino acids, including Lys85, Val135, Asp133, and Asp200, and His438, Ser198, and Thr120, yielding encouraging docking energies of −9.6 and −12.3 kcal/mol. Additionally, through extensive molecular dynamics simulations spanning a 100 ns trajectory, we established the robust stability of ligands DKS1 and DKS4 within the active pockets of GSK-3β and AChE. Particularly noteworthy was DKS5, which exhibited an outstanding human oral absorption rate of 79.792%, transcending the absorption rates observed for other molecules in our study. Conclusion: In summary, our in silico findings have illuminated the potential of our meticulously designed molecules as groundbreaking agents in the fight against Alzheimer’s disease, capable of simultaneously inhibiting both GSK-3β and BuChE. Full article

Carbon fiber-reinforced polymer (CFRP) components were extensively used and current studies mainly refer to CFRP laminates. The dynamic performance of the complicated CFRP antenna beams is yet to be explored. Therefore, a sensor layout based on fiber Bragg gratings (FBGs) in series was designed to measure the dynamic response of the CFRP antenna beam, and various vibration tests (sweep frequency test, simulated long-life vibration test, shock vibration test, functional vibration test, and constant frequency vibration test) were conducted. The time and frequency domain analysis on FBG sensing signals was performed to check the vibration performance and assess the health condition of this novel CFRP structure. The results indicate that strain values reach a maximum of only 300 µε under different test conditions. The antenna beam exhibited clear vibration patterns, with the first four intrinsic frequencies identified at 44, 94.87, 107.1, and 193.45 Hz. It shows that strain distribution and vibration modes of the antenna beam can be characterized from the sensing data, and the dynamic feature can be much more accurately assessed. The FBG sensors attached on the surface of CFRP antenna beam can accurately and stably measure the dynamic response, which validates that the interfaces between optical fiber sensing elements and CFRP material have excellent interfacial bonding characteristics. The novel CFRP antenna beam exhibits the excellent dynamic performance and stability, offering the replacement of traditional steel antenna beams. The study can finally instruct the development of self-sensing CFRP antenna beams assembled with FBGs in series. Full article

Over the past few years, target detectors that utilize Convolutional Neural Networks have gained extensive application in the domain of remote sensing (RS) imagery. Recently, optimizing bounding boxes has consistently been a hot topic in the research field. However, existing methods often fail to take into account the interference caused by the shape and orientation changes of RS targets with high aspect ratios during training, leading to challenges in boundary perception when dealing with RS targets that have large aspect ratios. To deal with this challenge, our study introduces the Adaptive Boundary Perception Network (ABP-Net), a novel two-stage approach consisting of pre-training and training phases, which enhances the boundary perception of CNN-based detectors. In the pre-training phase, involving the initialization of our model’s backbone network and the label assignment, the traditional label assignment with a fixed IoU threshold fails to fully cover the critical information of slender targets, resulting in the detector missing lots of high-quality positive samples. To overcome this drawback, we design a Shape-Sensitive (S-S) label assignment strategy that can improve the boundary shape perception by dynamically adjusting the IoU threshold according to the aspect ratios of the targets so that the high-quality samples with critical features can be divided into positive samples. Moreover, during the training phase, minor angle differences of the slender bounding box may cause a significant change in the value of the loss function, producing unstable gradients. Such drastic gradient changes make it difficult for the model to find a stable update direction when optimizing the bounding box parameters, resulting in difficulty with the model convergence. To this end, we propose the Robust–Refined loss function (R-R), which can enhance the boundary localization perception by focusing on low-error samples and suppressing the gradient amplification of difficult samples, thereby improving the model stability and convergence. Experiments on UCAS-AOD and HRSC2016 datasets validate our specialized detector for high-aspect-ratio targets, improving performance, efficiency, and accuracy with straightforward operation and quick deployment. Full article

GdmCl and NaSCN are two strong chaotropic salts commonly used in protein folding and stability studies, but their microscopic mechanisms remain enigmatic. Here, by CD and NMR, we investigated their effects on conformations, stability, binding and backbone dynamics on ps-ns and µs-ms time scales of a 39-residue but well-folded WW4 domain at salt concentrations ≤200 mM. Up to 200 mM, both denaturants did not alter the tertiary packing of WW4, but GdmCl exerted more severe destabilization than NaSCN. Intriguingly, GdmCl had only weak binding to amide protons, while NaSCN showed extensive binding to both hydrophobic side chains and amide protons. Neither denaturant significantly affected the overall ps-ns backbone dynamics, but they distinctively altered µs-ms backbone dynamics. This study unveils that GdmCl and NaSCN destabilize a protein before the global unfolding occurs with differential binding properties and µs-ms backbone dynamics, implying the absence of a simple correlation between thermodynamic stability and backbone dynamics of WW4 at both ps-ns and µs-ms time scales. Full article

Plant-derived PA10T is regarded as one of the most promising semi-aromatic polyamides; however, shortcomings, including low dimensional accuracy, high moisture absorption, and relatively high dielectric constant and loss, have impeded its extensive utilization. Polymer blending is a versatile and cost-effective method to fabricate new polymeric materials with excellent comprehensive performance. In this study, various ratios of PA10T/PPO blends were fabricated via melt blending with the addition of a SEBS-g-MAH compatibilizer. Molau test and scanning electron microscopy (SEM) were employed to study the influence of SEBS-g-MAH on the compatibility of PA10T and PPO. These studies indicated that SEBS-g-MAH effectively refines the domain size of the dispersed PPO phase and improves the dispersion stability of PPO particles within a hexafluoroisopropanol solvent. This result was attributed to the in situ formation of the SEBS-g-PA10T copolymer, which serves as a compatibilizer. Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) results showed that the melting–crystallization behavior and thermal stability of blends closely resembled that of pure PA10T. Dynamic mechanical analysis (DMA) revealed that as the PPO content increased, there was a decrease in the glass transition temperature and storage modulus of PA10T. The water absorption rate, injection molding shrinkage, dielectric properties, and mechanical strength of blends were also systematically investigated. As the PPO content increased from 10% to 40%, the dielectric loss at 2.5 GHz decreased significantly from 0.00866 to 0.00572, while the notched Izod impact strength increased from 7.9 kJ/m² to 13.7 kJ/m². Full article

Microgrids (MGs) have evolved as critical components of modern energy distribution networks, providing increased dependability, efficiency, and sustainability. Effective control strategies are essential for optimizing MG operation and maintaining stability in the face of changing environmental and load conditions. Traditional rule-based control systems are extensively used due to their interpretability and simplicity. However, these strategies frequently lack the flexibility for complex and changing system dynamics. This paper provides a novel method called hybrid intelligent control for adaptive MG that integrates basic rule-based control and deep learning techniques, including gated recurrent units (GRUs), basic recurrent neural networks (RNNs), and long short-term memory (LSTM). The main target of this hybrid approach is to improve MG management performance by combining the strengths of basic rule-based systems and deep learning techniques. These deep learning techniques readily enhance and adapt control decisions based on historical data and domain-specific rules, leading to increasing system efficiency, stability, and resilience in adaptive MG. Our results show that the proposed method optimizes MG operation, especially under demanding conditions such as variable renewable energy supply and unanticipated load fluctuations. This study investigates special RNN architectures and hyperparameter optimization techniques with the aim of predicting power consumption and generation within the adaptive MG system. Our promising results show the highest-performing models indicating high accuracy and efficiency in power prediction. The finest-performing model accomplishes an $R^2$ value close to 1, representing a strong correlation between predicted and actual power values. Specifically, the best model achieved an $R^2$ value of 0.999809, an MSE of 0.000002, and an MAE of 0.000831. Full article

In this study, we explored the dynamic field of fuzzy logic and artificial intelligence (AI) in financial analysis from 1990 to 2023. Utilizing the bibliometrix package in RStudio and data from the Web of Science, we focused on identifying mathematical models and the evolving role of fuzzy information granulation in this domain. The research addresses the urgent need to understand the development and impact of fuzzy logic and AI within the broader scope of evolving technological and analytical methodologies, particularly concentrating on their application in financial and banking contexts. The bibliometric analysis involved an extensive review of the literature published during this period. We examined key metrics such as the annual growth rate, international collaboration, and average citations per document, which highlighted the field’s expansion and collaborative nature. The results revealed a significant annual growth rate of 19.54%, international collaboration of 21.16%, and an average citation per document of 25.52. Major journals such as IEEE Transactions on Fuzzy Systems, Fuzzy Sets and Systems, the Journal of Intelligent & Fuzzy Systems, and Information Sciences emerged as significant contributors, aligning with Bradford’s Law’s Zone 1. Notably, post-2020, IEEE Transactions on Fuzzy Systems showed a substantial increase in publications. A significant finding was the high citation rate of seminal research on fuzzy information granulation, emphasizing its mathematical importance and practical relevance in financial analysis. Keywords like “design”, “model”, “algorithm”, “optimization”, “stabilization”, and terms such as “fuzzy logic controller”, “adaptive fuzzy controller”, and “fuzzy logic approach” were prevalent. The Countries’ Collaboration World Map indicated a strong pattern of global interconnections, suggesting a robust framework of international collaboration. Our study highlights the escalating influence of fuzzy logic and AI in financial analysis, marked by a growth in research outputs and global collaborations. It underscores the crucial role of fuzzy information granulation as a mathematical model and sets the stage for further investigation into how fuzzy logic and AI-driven models are transforming financial and banking analysis practices worldwide. Full article

Zika virus (ZIKV) is a positive-sense single-stranded virus member of the Flaviviridae family. Among other arboviruses, ZIKV can cause neurological disorders such as Guillain Barré syndrome, and it can have congenital neurological manifestations and affect fertility. ZIKV nonstructural protein 5 (NS5) is essential for viral replication and limiting host immune detection. Herein, we performed virtual screening to identify novel small-molecule inhibitors of the ZIKV NS5 methyltransferase (MTase) domain. Compounds were tested against the MTases of both ZIKV and DENV, demonstrating good inhibitory activities against ZIKV MTase. Extensive molecular dynamic studies conducted on the series led us to identify other derivatives with improved activity against the MTase and limiting ZIKV infection with an increased selectivity index. Preliminary pharmacokinetic parameters have been determined, revealing excellent stability over time. Preliminary in vivo toxicity studies demonstrated that the hit compound 17 is well tolerated after acute administration. Our results provide the basis for further optimization studies on novel non-nucleoside MTase inhibitors. Full article

In order to achieve multi-objective chassis coordination control for 4WID-4WIS (four-wheel independent drive–four-wheel independent steering) electric vehicles, this paper proposes a coordinated control strategy based on the extension dynamic stability domain. The strategy aims to improve trajectory tracking performance, handling stability, and economy. Firstly, expert PID and model predictive control (MPC) are used to achieve longitudinal speed tracking and lateral path tracking, respectively. Then, a sliding mode controller is designed to calculate the expected yaw moment based on the desired vehicle states. The extension theory is applied to construct the extension dynamic stability domain, taking into account the linear response characteristics of the vehicle. Different coordinated allocation strategies are devised within various extension domains, providing control targets for direct yaw moment control (DYC) and active rear steering (ARS). Additionally, a compound torque distribution strategy is formulated to optimize driving efficiency and tire adhesion rate, considering the vehicle’s economy and stability requirements. The optimal wheel torque is calculated based on this strategy. Simulation tests using the CarSim/Simulink co-simulation platform are conducted under slalom test and double-lane change to validate the control strategy. The test results demonstrate that the proposed control strategy not only achieves good trajectory tracking performance but also enhances handling stability and economy during driving. Full article

The regeneration of highly dynamic driving maneuvers on vehicle test benches is challenging due to several influences, such as power losses, vibrations in the overall system that involves the vehicle with the test bench, uncertainties in the model parameterization, and time delays from both the test bench and the measurement systems. In order to improve the dynamic response of the vehicle test bench and to overcome system disturbances, we employed different types of control algorithms for a mechanical multi-mass model. First, those controllers are extensively investigated in the frequency domain to analyze their stability and evaluate the noise rejection quality. Then, the expectations from the frequency analysis are confirmed in a time-domain simulation. Furthermore, sensitivity analysis tests were conducted to evaluate each controller’s robustness against the modeling parameters’ uncertainty. The linear quadratic controller with integral action demonstrated the best compromise between performance and robustness. Full article