Search Results (4,126)

Zeroing neural networks (ZNN), as a specialized class of bio-Iinspired neural networks, emulate the adaptive mechanisms of biological systems, allowing for continuous adjustments in response to external variations. Compared to traditional numerical methods and common neural networks (such as gradient-based and recurrent neural networks), this adaptive capability enables the ZNN to rapidly and accurately solve time-varying problems. By leveraging dynamic zeroing error functions, the ZNN exhibits distinct advantages in addressing complex time-varying challenges, including matrix inversion, nonlinear equation solving, and quadratic optimization. This paper provides a comprehensive review of the evolution of ZNN model formulations, with a particular focus on single-integral and double-integral structures. Additionally, we systematically examine existing nonlinear activation functions, which play a crucial role in determining the convergence speed and noise robustness of ZNN models. Finally, we explore the diverse applications of ZNN models across various domains, including robot path planning, motion control, multi-agent coordination, and chaotic system regulation. Full article

Constant luminous flux lamps are required for ensuring reliable and consistent illumination in various applications, including emergency lighting, outdoor activities, and general use. However, some activities may require maintaining a constant luminous flux, where the design must control the current during the use. This paper presents the design of a portable light-emitting diode (LED) lighting system powered by batteries that maintains constant luminous flux using the zero-average dynamic control (ZAD) and a proportional-integral-derivative (PID) controllers. This system can adapt the current to maintain the luminous flux required for reliable portable lighting applications used in outdoor activities. The results show that the system can provide constant illumination with 12-volt, 18-volt, and 24-volt batteries, and a 12-volt battery with a state of charge of 10%, enhancing usability for outdoor activities, emergency situations, and professional applications. Full article

This paper is centered around the autonomous displacement of the maglev ruler system, with the core objective of optimizing the performance of its horizontal control system. The horizontal positioning precision of the maglev ruler’s mover core plays a crucial role in determining the survey accuracy. Moreover, its horizontal system exhibits distinct nonlinear and strongly coupled characteristics. Initially, a mathematical model is meticulously established based on the principles of magnetic circuits and dynamics. By analyzing the relationship between the Ampere force and the coil current, and constructing the dynamic equations, it is clearly demonstrated that this system belongs to a nonlinear and strongly coupled type with two inputs and two outputs. Subsequently, the active disturbance rejection control algorithm is employed to address the decoupling issue, and a novel differential tracker, SYSTD, is designed. SYSTD is constructed using specific elementary functions. Through rigorous theoretical derivation, its favorable stability is verified. The basis for parameter selection is obtained through phase-plane analysis, and a detailed tuning method is provided. Additionally, a sensor-less control technology is proposed, where survey coils are wound along the horizontal control coils to estimate the position of the mover core. The simulation and experimental results indicate that the improved system showcases excellent performance. In the simulation, the positioning accuracy can reach ±5 μm, while, in the experiment, the control accuracy can achieve ±2 μm. It can effectively realize decoupling control and possesses good dynamic, static characteristics, as well as remarkable robustness. This research paves the way for the practical application of the maglev ruler system in fields such as coordinate survey. Full article

Grid-forming (GFM) converters play a vital role in future power systems due to their ability to independently establish voltage and frequency. However, their interaction with AC circuits may give rise to synchronous oscillations, which pose a threat to system stability and dynamic performance. This paper investigates the issue of synchronous oscillations and proposes an ultra-local model predictive control strategy for their suppression. First, a small-signal power dynamic model is developed to analyze the mechanism behind these oscillations. It is revealed that this problem is related to the electromagnetic dynamics of power transfer and is strongly influenced by the line impedance characteristics. Then, a predictive control framework is formulated, which incorporates oscillation suppression into the control objective and enables the real-time optimization of the active power reference. To avoid reliance on detailed system models, an ultra-local modeling approach is introduced. In this framework, a fixed-time sliding mode observer is employed to estimate the system power dynamics in real time, enabling the prediction of future states without requiring grid-side parameters and facilitating the design of a model-free controller. Simulation results verify that the proposed method effectively mitigates synchronous oscillations while significantly enhancing system stability and robustness. Full article

The transportation sector mainly relied on fossil fuel and is one of the major causes of climate change and environmental pollution. Advances in smart sensing technology are paving the way for the development of clean and intelligent vehicles that lead to a more sustainable transportation system. In response, the automotive industry is actively engaging in new sensor technologies and innovative control and diagnostic algorithms that improve energy sustainability and reduce vehicle emissions. In particular, recent regulations for diesel vehicles require the integration of smart soot sensors to deal with particulate filter on-board diagnostic (OBD) challenges. Meeting the recent, more stringent OBD requirements will be difficult using traditional diagnostic approaches. This study investigates an advanced diagnostic strategy to assess particulate filter health based on resistive soot sensors and available engine variables. The sensor data are projected to generate a 2D signature that reflects the changes in filtration efficiency. A relevant feature (character) is then extracted from the generated signature that can be transformed into an analytical expression used as an indicator of DPF malfunction. The diagnostic strategy uses an adaptive approach that dynamically adjusts the signature’s characters according to the engine’s operating conditions. A correction factor is calculated using an optimization algorithm based on the integral of engine speed measurements and IMEP set points during each sensor loading period. Different cost functions have been tested and evaluated to improve the diagnostic performance. The proposed adaptive approach is model-free and eliminates the need for subsystem models, iterative algorithms, and extensive calibration procedures. Furthermore, the time-consuming and inaccurate estimation of soot emissions upstream of the DPF is avoided. It was evaluated on a validated numerical platform under NEDC driving conditions with simultaneous dispersions on engine-out soot concentration and soot sensor measurements. The promising results highlight the robustness and superior performance of this approach compared to a diagnostic strategy solely reliant on sensor data. Full article

In this study, droplet digital PCR and deep bisulfite sequencing were used to study the absolute and active rDNA copy number (CN) and the effect of paternal age on human and mouse sperm. The absolute CN ranged from 98 to 404 (219 ± 47) in human and from 98 to 177 (133 ± 14) in mouse sperm. Methylation of the human upstream control element/core promoter (UCE/CP) region and the 5′ external transcribed spacer, as well as that of the mouse CP, the spacer promoter, and 28S rDNA, significantly increased with donor age and absolute CN. Overall, rDNA hypomethylation was much more pronounced in mouse sperm, with 101.7 ± 11.4 copies showing a completely (0%) unmethylated and 11.3 ± 2.8 (8.5%) a slightly methylated (1–10%) CP region, compared to humans with 25.7 ± 9.5 (12%) completely unmethylated and 83.0 ± 19.8 slightly methylated UCE/CP regions. Although the absolute CN was much higher in human sperm, the number of copies with a hypomethylated (0–10%) promoter was comparable in humans (108.7 ± 28.3) and mice (113.0 ± 12.2). However, in mice, the majority (77%) of all copies were completely unmethylated, whereas in humans a high percentage (38%) showed one or two single CpG methylation errors. These different germline methylation dynamics may be due to species differences in reproductive strategies and lifespan. Complete demethylation of the sperm rDNA promoter in mice may be essential for embryonic genome activation, which already occurs at the 2-cell stage in mice and at the 4–8-cell stage in humans. The paternal age effect has been conserved between humans and mice with some notable differences. In humans, the number of hypomethylated (0–10%) copies decreased with age, whereas in mice only the completely unmethylated copies decreased with age. The number of methylated rDNA copies (>1% in mice and >10% in humans) significantly increased with age. Full article

Robust credit risk prediction in emerging economies increasingly demands the integration of external factors (EFs) beyond borrowers’ control. This study introduces a scenario-based methodology to incorporate EF—namely COVID-19 severity (mortality and confirmed cases), climate anomalies (temperature deviations, weather-induced road blockages), and social unrest—into machine learning (ML) models for credit delinquency prediction. The approach is grounded in a CRISP-DM framework, combining stationarity testing (Dickey–Fuller), causality analysis (Granger), and post hoc explainability (SHAP, LIME), along with performance evaluation via AUC, ACC, KS, and F1 metrics. The empirical analysis uses nearly 8.2 million records compiled from multiple sources, including 367,000 credit operations granted to individuals and microbusiness owners by a regulated Peruvian financial institution (FMOD) between January 2020 and September 2023. These data also include time series of delinquency by economic activity, external factor indicators (e.g., mortality, climate disruptions, and protest events), and their dynamic interactions assessed through Granger causality to evaluate both the intensity and propagation of external shocks. The results confirm that EF inclusion significantly enhances model performance and robustness. Time-lagged mortality (COVID MOV) emerges as the most powerful single predictor of delinquency, while compound crises (climate and unrest) further intensify default risk—particularly in portfolios without public support. Among the evaluated models, CNN and XGB consistently demonstrate superior adaptability, defined as their ability to maintain strong predictive performance across diverse stress scenarios—including pandemic, climate, and unrest contexts—and to dynamically adjust to varying input distributions and portfolio conditions. Post hoc analyses reveal that EF effects dynamically interact with borrower income, indebtedness, and behavioral traits. This study provides a scalable, explainable framework for integrating systemic shocks into credit risk modeling. The findings contribute to more informed, adaptive, and transparent lending decisions in volatile economic contexts, relevant to financial institutions, regulators, and risk practitioners in emerging markets. Full article

The objective of this study was to enhance the tracking effectiveness of the position adjustment for the electronic throttle in electric vehicles, as well as boost fuel efficiency and the dynamic performance of the vehicles. Firstly, a mathematical model, which pertains to the electronic throttle system, is established, and subsequently, the nonlinear uncertain system is made into a linear uncertain system. Subsequently, a self-coupling PID control law is designed, and an analysis is conducted on the system’s stability and its capacity to reject disturbances. Secondly, taking into consideration that the parameters of the PID controller with self-coupling mechanism are related to the system’s response speed, disturbance rejection capability, and overshoot, a self-adjusting speed factor transition function is put forward to address the conflict between speed and overshoot. Finally, numerical simulations and experimental tests are carried out. The results verify that, compared with the conventional PID controller, ADRC (Active Disturbance Rejection Control), and fuzzy PID, the proposed controller has a faster response speed, higher control accuracy, and better robustness. Full article

The Soil Moisture Active Passive (SMAP) satellite can measure sea surface winds under tropical cyclone (TC) conditions with its L-band microwave radiometer, without being affected by rainfall or signal saturation. Through the statistical analysis of SMAP data, this study aims to develop radial wind profile models for the TC outer area whose distance from TC center is larger than the radius of maximum wind ($R_{\mathrm{m}}$). A total of 196 TC cases observed by SMAP were collected between 2015 and 2020, and their intensities range from tropical storm to category 5. Based on the wind and radius data, the key model parameters $\alpha$ and $\beta$ were fitted through the Rankine vortex model and the tangential wind profile (TWP) Gaussian model, respectively. $\alpha$ and $\beta$ control the rate of change of the tangential wind speed with radius. Subsequently, for the parametric representation of $\alpha$ and $\beta$, we extracted some TC wind filed parameters, such as maximum wind speed ($U_{\mathrm{m}}$), $R_{\mathrm{m}}$, the average wind speed at $R_{\mathrm{m}}$ ($U_{\mathrm{ma}}$), and the average radius of 17 m/s ($R_{17}$) and examined the relationship between $U_{\mathrm{ma}}$ and $U_{\mathrm{m}}$, the relationship between $R_{\mathrm{m}}$ and $R_{17}$, the relationship between $\alpha$, $U_{\mathrm{m}}$ and $R_{\mathrm{m}}$, and the relationship between $\beta$, $U_{\mathrm{m}}$ and $R_{\mathrm{m}}$. According to the results, the new radial wind profile models were proposed, i.e., SMAP Rankine Model-4 (SRM-4), SMAP Rankine Model-5 (SRM-5), and SMAP Gaussian Model-1 (SGM-1). A significant advantage of these models is that they can simulate average wind distribution through the conversion from $U_{\mathrm{m}}$ to $U_{\mathrm{ma}}$. Finally, comparisons were made between the new models and existing SRM-1, SRM-2, and SRM-3, according to the Advanced Microwave Scanning Radiometer 2 (AMSR-2) measurements of 126 TC cases. The results demonstrate that the SRM-4 simulated the radial wind profile best overall, with the lowest root mean-square error (RMSE) of 5.57 m/s, due to replacing the parameter $U_{\mathrm{m}}$ with $U_{\mathrm{ma}}$, using Rankine vortex for α parameterization and modeling with adequate data. Moreover, the models outperform in the Atlantic Ocean, with a RMSE of 5.37 m/s. The new models have the potential to make a contribution to the study of ocean surface dynamics and be used for forcing numerical models under TC conditions. Full article

Granite is widely used in laboratory rockburst simulations due to its exceptional strength, brittleness, and uniform composition. This study employs a true triaxial loading system to replicate asymmetric stress states near free surfaces, allowing precise control of three-dimensional stresses to simulate strain-mode rockbursts. Advanced monitoring tools, such as acoustic emission (AE) and high-speed imaging, were used to investigate the evolution process, failure mechanisms, and monitoring strategies. The evolution of strain-mode rockbursts is divided into five stages: stress accumulation, crack initiation, critical instability, rockburst occurrence, and residual stress adjustment. Each stage exhibits dynamic responses and progressive energy release. Failure is governed by a tension–shear coexistence mechanism, where vertical splitting and diagonal shear fractures near free surfaces lead to V-shaped craters and violent rock fragment ejection. This reflects the brittle nature of granite under high-stress conditions. The AE monitoring proved highly effective in identifying rockburst precursors, with key indicators including quiet periods of low AE activity and sudden surges in AE counts, coupled with ‘V-shaped’ b-value troughs, offering reliable early warning signals. These findings provide critical insights into strain-mode rockburst dynamics, highlighting the transition from elastic deformation to dynamic failure and the role of energy release mechanisms. Full article

RNA-dependent RNA polymerase (RdRP) represents a critical target for antiviral drug development. We developed a multi-model machine learning framework combining five traditional algorithms (ExtraTreesClassifier, RandomForestClassifier, LGBMClassifier, BernoulliNB, and BaggingClassifier) with a CNN deep learning model to identify potential RdRP inhibitors among FDA-approved drugs. Using the PubChem dataset AID 588519, our ensemble models achieved the highest performance with accuracy, ROC-AUC, and F1 scores higher than 0.70, while the CNN model demonstrated complementary predictive value with a specificity of 0.77 on external validation. Molecular docking studies with the norovirus RdRP (PDB: 4NRT) identified raloxifene as a promising candidate, with a binding affinity (−8.8 kcal/mol) comparable to the positive control (−9.2 kcal/mol). The molecular dynamics simulation confirmed stable binding with RMSD values of 0.12–0.15 nm for the protein–ligand complex and consistent hydrogen bonding patterns. Our findings suggest that raloxifene may possess RdRP inhibitory activity, providing a foundation for its experimental validation as a potential broad-spectrum antiviral agent. Full article

The application of exoskeleton robots has demonstrated promising effectiveness in promoting the recovery of motor skills in patients with upper limb dysfunction. However, the joint misalignment caused by rigid exoskeletons usually leads to an uncomfortable experience for users. In this work, an adaptive cable-driven elbow exoskeleton inspired by the structural characteristics of the shrimp shell was developed to facilitate the rehabilitation of the elbow joint and to provide more compliant human-exoskeleton interactions. The exoskeleton was specifically designed for elbow flexion and extension, with a total weight of approximately 0.6 kg. Based on the mechanical design and cable configuration of the exoskeleton, the kinematics and dynamics of driving cables were analyzed. Subsequently, a PID-based control strategy was designed with cable kinematics. To evaluate the practical performance of the proposed exoskeleton in elbow assistance, a prototype was established and experimented with six subjects. According to the experimental results, the measured elbow joint angle trajectory is generally consistent with the desired trajectory, with a mean position tracking accuracy of approximately 0.997, which supports motion stability in rehabilitation scenarios. Meanwhile, the collected sEMG values from biceps brachii and brachioradialis under the exoskeleton condition show a significant reduction in average muscle activation by 37.7% and 28.8%, respectively, compared to the condition without exoskeleton. Full article

When a mobile robot on an overhead line carries out operations, the effects of the elastic deformation and vibration of the flexible overhead line on motion performance cannot be ignored. This study proposes a method for active compensation of the robot’s trajectory, based on the force–deformation characteristics of the overhead line. Overhead line mobile robot systems show a complex nonlinear coupled vibration problem. To simplify the flexible environment, it is modeled as a single-degree-of-freedom spring–damped system. A rigid–flexible coupled dynamics model is established using the sub-bar method and the Lagrangian method. A numerical simulation is used to compare and analyze the end trajectories of mobile robots using generalized coordinates when the overhead line is rigid and flexible, respectively, revealing the coupling mechanism between the flexible overhead line and the robot. Based on the force–deformation characteristics of the overhead line, an active robot trajectory compensation method is proposed. The experimental results show that the established rigid–flexible coupling dynamics model describes the dynamic characteristics of an overhead line mobile robot, and the active robot trajectory compensation method has certain feasibility. The proposed method provides a reference basis for the control of overhead line mobile robots and has some applicability in addressing motion compensation issues in flexible environments. Full article

Insects are among the most diverse and abundant organisms on Earth, and their population dynamics are strongly influenced by entomopathogenic fungi. This study examines the role of carbon and nitrogen metabolism in the virulence of the entomopathogenic fungus Metarhizium rileyi against the migratory locust, Locusta migratoria. The findings demonstrate that the capacity of M. rileyi to utilize different carbon and nitrogen sources is a key factor in its virulence. Specifically, two strains of M. rileyi (PPDB201006 and SZCY201010) exhibited distinct metabolic abilities, with PPDB201006 displaying superior growth and enzyme activities on various carbon and nitrogen sources compared to SZCY201010. These metabolic differences were associated with significant variations in virulence, as PPDB201006 induced higher mortality rates in L. migratoria than SZCY201010. Metabolomics analysis revealed that infection by M. rileyi led to substantial alterations in the hemolymph metabolites of L. migratoria, particularly in organic acids, amino acids, sugars, and lipids. These results emphasize the significance of carbon and nitrogen metabolism in the pathogenicity of entomopathogenic fungi and offer new perspectives for optimizing their application as biological control agents. This study not only improves our understanding of fungal virulence mechanisms but also contributes to the development of more effective and sustainable pest management strategies. Full article

This research focuses on the development, design, implementation, and testing (with complete hardware and software integration) of a 6D Electromagnetic Actuation (EMA) system for the precise control and navigation of micro/nanorobots (MNRs) in high-viscosity fluids, addressing critical challenges in targeted drug delivery within complex biological environments, such as blood vessels. The primary objective is to overcome limitations in the actuation efficiency, trajectory stability, and accurate path-tracking of MNRs. The EMA system utilizes three controllable orthogonal pairs of Helmholtz coils to generate uniform magnetic fields, which magnetize and steer MNRs in 3D for orientation. Another three controllable orthogonal pairs of Helmholtz coils generate uniform magnetic fields for the precise 3D orientation and steering of MNRs. Additionally, three orthogonal pairs of Maxwell coils generate uniform magnetic field gradients, enabling efficient propulsion in dynamic 3D fluidic environments in real time. This hardware configuration is complemented by three high-resolution digital microscopes that provide real-time visual feedback, enable the dynamic tracking of MNRs, and facilitate an effective closed-loop control mechanism. The implemented closed-loop control technique aimed to enhance trajectory accuracy, minimize deviations, and ensure the stable movement of MNRs along predefined paths. The system’s functionality, operation, and performance were tested and verified through various experiments, focusing on hardware, software integration, and the control algorithm. The experimental results show the developed system’s ability to activate MNRs of different sizes (1 mm and 0.5 mm) along selected desired trajectories. Additionally, the EMA system can stably position the MNR at any point within the 3D fluidic environment, effectively counteracting gravitational forces while adhering to established safety standards for electromagnetic exposure to ensure biocompatibility and regulatory compliance. Full article