Search Results (16,458)

The central purpose of this study is to develop and validate an advanced numerical model capable of simulating the vibrational behavior of the operator’s seat in a tractor-type agricultural vehicle designed for operation in protected horticultural environments, such as vegetable greenhouses. The three-dimensional (3D) model of the seat was created using SolidWorks 2023, while its dynamic response was investigated through Finite Element Analysis (FEA) in Altair SimSolid, enabling a detailed evaluation of the natural vibration modes within the 0–80 Hz frequency range. Within this interval, eight significant natural frequencies were identified and correlated with the real structural behavior of the seat assembly. For experimental validation, direct time-domain measurements were performed at a constant speed of 5 km/h on an uneven, grass-covered dirt track within the research infrastructure of INMA Bucharest, using the TE-0 self-propelled electric tractor prototype. At the operator’s seat level, vibration data were collected considering the average anthropometric characteristics of a homogeneous group of subjects representative of typical tractor operators. The sample of participating operators, consisting exclusively of males aged between 27 and 50 years, was selected to ensure representative anthropometric characteristics and ergonomic consistency for typical agricultural tractor operators. Triaxial accelerometer sensors (NexGen Ergonomics, Pointe-Claire, Canada, and Biometrics Ltd., Gwent, UK) were strategically positioned on the seat cushion and backrest to record accelerations along the X, Y, and Z spatial axes. The recorded acceleration data were processed and converted into the frequency domain using Fast Fourier Transform (FFT), allowing the assessment of vibration transmissibility and resonance amplification between the floor and seat. The combined numerical–experimental approach provided high-fidelity validation of the seat’s dynamic model, confirming the structural modes most responsible for vibration transmission in the 4–8 Hz range—a critical sensitivity band for human comfort and health as established in previous studies on whole-body vibration exposure. Beyond validating the model, this integrated methodology offers a predictive framework for assessing different seat suspension configurations under controlled conditions, reducing experimental costs and enabling optimization of ergonomic design before physical prototyping. The correlation between FEA-based modal results and field measurements allows a deeper understanding of vibration propagation mechanisms within the operator–seat system, supporting efforts to mitigate whole-body vibration exposure and improve long-term operator safety in horticultural mechanization. Full article

We present a high-resolution infrared spectroscopic study of four vibrational bands of the ¹³CF₄ isotopologue: the symmetric stretching fundamental ${\nu }_1$, the triply degenerate bending mode ${\nu }_4$, the combination band ${\nu }_1+{\nu }_4$, and the hot band ${\nu }_1-{\nu }_4$. A global analysis was performed using a tensorial formalism adapted to the $T_{\mathrm{d}}$ symmetry group, allowing for consistent modelling of rotational structures. Reduced energy levels were extracted and fitted simultaneously for the four levels, yielding precise spectroscopic constants. The derived parameters enhance the spectroscopic characterization of ¹³CF₄, a species of interest for isotopic studies and environmental monitoring. A total of 8992 transitions were assigned in the investigated spectral regions. The quality of fit is confirmed by a root mean square (RMS) deviation of about 0.0022 cm⁻¹, highlighting the accuracy of the effective Hamiltonian model. These results provide a robust framework for future line list development and integration into spectroscopic databases such as TFMeCaSDa, HITRAN and GEISA. Full article

This paper presents an experimental longitudinal mode control approach for a biomimetic underwater robot. Input–output models for surge velocity and pitch angle were derived through experiments, considering the fish robot body with servo motors and control pins as a single system to solve the problem of fish robots, which are complex and nonlinear, and also contain uncertainty. Closed-loop control systems were designed using PID controllers based on these models, and their performance was verified through simulations and experiments. Surge velocity and pitch angle response models were developed for nominal surge velocities of 0.2 m/s and 0.4 m/s. The surge velocity response models exhibited high agreement rates of 75.25% and 81.23% between the identified linear models and experimental results at 0.2 m/s and 0.4 m/s, respectively. In contrast, the pitch angle response model showed lower agreement rates of 68.02% and 34.24% between the identified linear model and experimental results at 0.2 m/s and 0.4 m/s, respectively. The gain margin and phase margin of the surge controller were 28.7 dB and 116°, and 37.2 dB and 70.6°, respectively. For the pitch response model, the low-frequency gain of the transfer function was very small at −31 dB when the nominal surge velocity was 0.2 m/s; this gain increased to −8 dB when the nominal surge velocity was increased to 0.4 m/s. It was observed that the initial value responses of the pitch angle converged to 0° with some oscillations in both the simulations and experiments. Therefore, it is believed that by identifying a linear model and subsequently designing a controller based on it, the surge velocity of the fish robot can be effectively controlled while stabilizing its pitch angle. Full article

Collaborative clustering is an ensemble technique that enhances clustering performance by simultaneously and synergistically processing multiple data dimensions or tasks. This is an active research area in artificial intelligence, machine learning, and data mining. A common approach to co-clustering is based on non-negative matrix factorization (NMF). While widely used, NMF-based co-clustering is limited by its bilinear nature and fails to capture the multilinear structure of data. With the objective of enhancing the effectiveness of non-negative Tucker decomposition (NTD) in image clustering tasks, in this paper, we propose a dual-graph constrained sparse non-negative Tucker decomposition NTD (GDSNTD) model for co-clustering. It integrates graph regularization, the Frobenius norm, and an $l_1$ norm constraint to simultaneously optimize the objective function. The GDSNTD mode, featuring graph regularization on both factor matrices, more effectively discovers meaningful latent structures in high-order data. The addition of the $l_1$ regularization constraint on the factor matrices may help identify the most critical original features, and the use of the Frobenius norm may produce a more highly stable and accurate solution to the optimization problem. Then, the convergence of the proposed method is proven, and the detailed derivation is provided. Finally, experimental results on public datasets demonstrate that the proposed model outperforms state-of-the-art methods in image clustering, achieving superior scores in accuracy and Normalized Mutual Information. Full article

The blade is a core component of the gas turbine, and blade fouling is characterized by highly concealed failure modes in the early stages and significant destructive potential in later stages. To address the lack of intelligence in early warning systems for compressor fouling, this study proposes a data-driven approach combining a digital-twin-based dynamic simulation model with the Weibull Proportional Hazards Model (WPHM) algorithm to enable reliable fault early warning. A modular design methodology was first adopted to construct a digital gas turbine model of the gas–gas combined power system on a dynamic simulation platform. High-fidelity fault simulation data were then generated to represent both healthy and faulty operating conditions. Through data governance and uncertainty quantification, key parameters influencing compressor fouling were identified. The Pearson correlation coefficient was applied to screen the most sensitive indicators, ensuring effective input selection for the prognostic model. Using historical health data from the simulation platform, the WPHM algorithm was trained to learn degradation patterns and establish a baseline failure risk model. This trained WPHM was then deployed to monitor real-time performance trends and provide early warnings for compressor blade fouling. Validation results from multi-unit simulations show that the proposed method achieves a fault warning rate of 95.0%, demonstrating its effectiveness and readiness to meet practical engineering requirements. Full article

This study investigated the influence of the cooling process on the microstructure and mechanical properties of high-strength, high-ductility ship plate steel. The transformation temperature ranges for ferrite (F) and bainite (B) for the experimental steel were determined through thermal simulation experiments. Based on these findings, hot-rolling experiments in laboratory were designed to elucidate the influence of three different cooling paths on the resultant microstructure and mechanical properties. The results demonstrate that the two-stage (air cooling + water cooling) and three-stage (water cooling + air cooling + water cooling) processes after rolling enhance the strength through phase transformation and precipitation strengthening mechanisms. The three-stage process provides an additional fine-grain strengthening effect. Compared to the F+Pearlite (P) or B microstructures produced by single-stage cooling, the F+B dual-phase steel obtained through these multi-stage cooling routes exhibits superior ductility at a comparable yield strength grade. Notably, the two-stage cooling mode proves particularly effective in enhancing ductility. These findings provide a theoretical foundation for designing cooling processes for high-strength, high-ductility ship plate steel. Full article

Prehabilitation programs for abdominal pre-operative patients are increasingly recognized for improving surgical outcomes, reducing post-operative complications, and enhancing recovery. Internet of Things (IoT)-enabled human movement monitoring systems offer promising support in mixed-mode settings that combine clinical supervision with home-based independence. These systems enhance accessibility, reduce pressure on healthcare infrastructure, and address geographical isolation. However, current implementations often lack personalized movement analysis, adaptive intervention mechanisms, and real-time clinical integration, frequently requiring manual oversight and limiting functional outcomes. This review-based paper proposes a conceptual framework informed by the existing literature, integrating Digital Twin (DT) technology, and machine learning/Artificial Intelligence (ML/AI) to enhance IoT-based mixed-mode prehabilitation programs. The framework employs inertial sensors embedded in wearable devices and smartphones to continuously collect movement data during prehabilitation exercises for pre-operative patients. These data are processed at the edge or in the cloud. Advanced ML/AI algorithms classify activity types and intensities with high precision, overcoming limitations of traditional Fast Fourier Transform (FFT)-based recognition methods, such as frequency overlap and amplitude distortion. The Digital Twin continuously monitors IoT behavior and provides timely interventions to fine-tune personalized patient monitoring. It simulates patient-specific movement profiles and supports dynamic, automated adjustments based on real-time analysis. This facilitates adaptive interventions and fosters bidirectional communication between patients and clinicians, enabling dynamic and remote supervision. By combining IoT, Digital Twin, and ML/AI technologies, the proposed framework offers a novel, scalable approach to personalized pre-operative care, addressing current limitations and enhancing outcomes. Full article

The variable speed pump-turbine is usually used to adjust the rotational speed to improve the efficiency in turbine mode and change the input power in pump mode because its rotational speed can vary within a certain range. In order to explore the evolutions of pressure pulsation and flow patterns caused by changes in the rotational speeds, the steady operating conditions under different rotational speeds in turbine and pump modes were investigated by using three-dimensional numerical simulations. The results show that as the pump-turbine operates with the highest efficiency at the rated rotational speed, the change in the rotational speed leads to the variation in macro-parameters, deterioration of the flow patterns, and increase in pressure pulsations. In addition, under a certain guide vane opening, with the increase in the rotational speed, the torque, power, and discharge increase in the turbine mode, while these parameters decrease in the pump mode. And when the rotational speed is too high or too low, it causes an obvious increase in pressure pulsations. Full article

LiDAR technology has undergone significant advancement in recent years, establishing itself as a technique for long-range, high-precision detection. As its use expands into more intricate scenarios, the need to overcome blind spots in the scanning field and enhance system stability has become increasingly critical. This paper introduces a novel coaxial LiDAR system featuring a double-clad optical fiber-based receiver which consists of a single-mode fiber core for the emission of the laser beam and a multimode inner cladding for the collection and transmission of the back-reflected beam. The real-time system is specifically engineered to measure distances in both near and far fields, eliminating blind spots. Experimental evaluations demonstrate that our system achieves a detection range of 0.2–70.7 m, with a distance accuracy of 3.4 cm and an angular resolution of 0.018°. Compared with conventional LiDAR systems, our approach eliminates the need for complex optical pathway designs and algorithmic compensation. It offers a simplified structure, enhanced stability, and high accuracy. Full article

Conventional whole-blood flow assays for quantifying thrombus formation are typically performed at room temperature and are technically demanding, which limits their translational applicability. We engineered a novel, disposable, mountable, and single-channel microfluidic chip (MC-2S), which is based on the Maastricht chamber (MC) and designed for automated evaluation of platelet function, coagulation and fibrinolysis under physiological conditions. The MC-2S chip allows customizable choices of thrombogenic surfaces, such as collagen and tissue factor. The chip was used in combination with an adapted, 1.3 kg brightfield/fluorescence microscope, operating at physiological temperature (37 °C), and with scripts for automated multicolor analysis of image features. The integrated system enables a robust, rapid, and high-content quantification of the kinetics of thrombus formation and dissolution. In platelet-sensitive mode, MC-2S demonstrated high sensitivity to antiplatelet therapy with aspirin or cangrelor. In coagulation-sensitive mode, it detected the anticoagulant effect of rivaroxaban plus its reversal by andexanet-α. In fibrinolysis-sensitive mode, it monitored tissue-type plasminogen activator-induced thrombus dissolution, inhibited by tranexamic acid. Collectively, the MC-2S platform was found to provide a versatile, physiologically relevant tool for functional hemostasis testing, with high potential for the acute and subacute evaluation of patient blood samples in the context of bleeding disorders, thrombosis risk, and drug monitoring. Full article

A sliding mode predictive control (SMPC) scheme integrated with an extreme learning machine (ELM) disturbance observer is proposed for the trajectory tracking of a flexible air-breathing hypersonic vehicle (FAHV). To streamline the controller design, the longitudinal model is decoupled into a velocity subsystem and an altitude subsystem. For the velocity subsystem, a proportional-integral sliding mode surface is designed, and the control law is derived by minimizing a cost function that weights the predicted sliding mode surface and the control input. For the altitude subsystem, a backstepping control framework is adopted, with the SMPC strategy embedded in each step. Multi-source disturbances are modeled as composite additive disturbances, and an ELM-based neural network observer is constructed for their real-time estimation and compensation, thereby enhancing system robustness. The semi-globally uniformly ultimately bounded (SGUUB) stability of the closed-loop system is rigorously proven using Lyapunov stability theory. Simulation results demonstrate the comprehensive superiority of the proposed method: it achieves reductions in Root Mean Square Error (RMSE) of 99.60% and 99.22% for velocity and altitude tracking, respectively, compared to Prescribed Performance Control with Backstepping Control (PPCBSC), and reductions of 98.48% and 97.12% relative to Terminal Sliding Mode Control (TSMC). Under parameter uncertainties, the developed ELM observer outperforms RBF-based observer and Extended State Observer (ESO) by significantly reducing tracking errors. These findings validate the high precision and strong robustness of the proposed approach. Full article

Cold-bonded (CB) fly ash aggregate, an eco-friendly material derived from industrial by-products, is used to fully replace natural coarse aggregate in producing lightweight concrete (LWC-CB). This study systematically investigates the post-high-temperature mechanical properties and damage mechanisms of LWC-CB. Specimens exposed to ambient temperature (10 °C) and elevated temperatures (200 °C, 400 °C, 600 °C) underwent cubic compression tests, with surface deformation monitored via digital image correlation (DIC). Experimental results indicate that the strength retention of LWC-CB is approximately 6% superior to ordinary concrete below 500 °C, beyond which its performance converges. Damage analysis reveals a transition in failure mode: at ambient temperature, shear failure is governed by the low intrinsic strength of CB aggregates, while after high-temperature exposure, damage localizes within the mortar and the interfacial transition zone (ITZ) due to mortar micro-cracking and thermal mismatch. To elucidate these mechanisms, a three-dimensional mesoscale model was developed and validated, effectively characterizing the internal multiphase structure at room temperature. Furthermore, a homogenization model was established to analyze the macroscopic thermo-mechanical response. The numerical simulations show strong agreement with experimental data, with a maximum deviation of 15% at 10 °C and 3% after high-temperature exposure, confirming the model’s accuracy in capturing the performance evolution of LWC-CB. Full article

Non-alcoholic fatty liver disease (NAFLD) represents a prevalent chronic hepatic disorder worldwide, with its incidence continuing to rise in recent years. At the core of its pathological progression lie multiple interconnected mechanisms, including dysregulated lipid metabolism (e.g., abnormal accumulation of triglycerides in hepatocytes), impaired insulin sensitivity (which exacerbates hepatic lipid deposition), excessive production of reactive oxygen species (ROS) leading to oxidative stress, and sustained low-grade chronic inflammation that further amplifies liver tissue damage. Saponins have emerged as a crucial research direction for NAFLD intervention due to their advantage of multi-target regulation. This review synthesizes the mode of action of commonly studied saponins, including triterpenoid saponins and steroidal saponins: they regulate lipid metabolism by inhibiting fatty acid synthesis; modulate the gut microbiota; scavenge reactive oxygen species (ROS); alleviate endoplasmic reticulum stress; exert anti-inflammatory effects by inhibiting inflammasomes; and simultaneously regulate autophagy, restrain the activation of hepatic stellate cells, and modulate the gut microbiota, thereby achieving anti-apoptotic and anti-hepatic fibrosis effects. In conclusion, saponins can synergistically intervene in NAFLD through multiple mechanisms with good safety, while low bioavailability constitutes the main bottleneck for their clinical translation. In the future, it is necessary to further optimize formulation processes to improve absorption efficiency and conduct high-quality clinical studies to verify their long-term efficacy and drug–drug interactions, thus providing a new possible direction for NAFLD treatment. Full article

Switchable microlasers with multicolor output and high spectral purity are of crucial importance for various photonic devices. However, switchable multicolor lasing usually operates in multimode, which largely restricts its practical applications due to the lack of an effective mode selection mechanism. Here, switchable single-mode lasing was successfully achieved in coupled microfiber cavities, in which each microfiber served as both WGM resonator and mode filter for another microfiber. The unique mode selection mechanism is demonstrated experimentally and theoretically in the coupled microfibers. Furthermore, the color of single-mode lasing is tunable at will via the doping of microfibers with different active materials. Our work might provide a platform for building switchable multicolor lasers and gaining further insights into photonic integration. Full article

Marine and island power systems usually incorporate various forms of energy supply, which poses challenges to the coordinated control of the system under diverse, irregular, and complex load operation modes. To improve the stability and self-sufficiency of island-isolated microgrids with high penetration of renewable energy, this study proposes a coordinated control strategy for an island microgrid with PV, HGT, and HESS, combining primary power allocation via low-pass filtering with a fuzzy logic-based secondary correction. The fuzzy controller dynamically adjusts power distribution based on the states of charge of the battery and supercapacitor, following a set of predefined rules. A comprehensive system model is developed in Matlab R2023b, integrating PV generation, an electrolyzer, HGT and a battery–supercapacitor HESS. Simulation results across four operational cases demonstrate that the proposed strategy reduces DC bus voltage fluctuations to a maximum of 4.71% (compared to 5.63% without correction), with stability improvements between 0.96% and 1.55%. The HESS avoids overcharging and over-discharging by initiating priority charging at low SOC levels, thereby extending service life. This work provides a scalable control framework for enhancing the resilience of marine and island microgrids with high renewable energy penetration. Full article