Search Results (2,323)

The growth of human activity in cities is a key factor in the degradation of air quality. Numerous studies have demonstrated the link between air quality and the existence of dangerous and chronic diseases that are extremely costly for individuals and society. This study presents an analytical framework that compares fixed and mobile air-quality monitoring approaches in cities with limited resources, using Mohammedia city, Morocco, as an example. The framework centers on mobile monitoring units mounted on vehicles and equipped with affordable sensors, GPS technology, and wireless communication systems to track important pollutants, including fine particulate matter (PM_2.5 and PM₁₀) and harmful gaseous compounds (NO₂, SO₂, CO, O₃). The evaluation relies on scenario-based modeling, performance data from existing literature, and calculations of costs throughout the system’s lifetime. To enhance measurement reliability, the researchers developed a correction system that addresses measurement errors caused by temperature, humidity, vehicle speed, vibrations, traffic-related interference, operational interruptions, and communication limitations. The findings indicate that fixed monitoring stations deliver superior measurement precision, with estimated uncertainty ranging from ±1.2–2.5%, though their coverage area is restricted to 0.534 km² (representing 1.6% of Mohammedia). In comparison, the suggested mobile setup could potentially monitor 9.8 km², covering approximately 30% of the city, while decreasing infrastructure needs and setup time (2–4 h compared to 2–4 weeks). Over 10 years, the total cost is EUR 252,000 for mobile monitoring, compared with EUR 3.6 million for a network of 20 fixed stations. These results demonstrate that corrected mobile monitoring systems offer significant promise as an economical and sustainable approach for managing urban environmental conditions. Full article

To address the detuning sensitivity of conventional linear vibration absorbers under wide-frequency harmonic excitation and the limited effectiveness of nonlinear energy sinks (NESs) in low-energy regimes, this study investigates a bistable nonlinear energy sink (BNES) based on a negative-stiffness support. A coupled model of the primary system and the BNES is established, and the analytical steady-state amplitude–frequency relationship of the system is derived using the harmonic balance method. The accuracy of the analytical solutions is verified through numerical integration. Based on the first Lyapunov method, the instability regions of the system are identified, and the effects of system parameters on the amplitude–frequency response of the primary structure are analyzed. On this basis, a comprehensive performance index that accounts for both peak suppression and average vibration reduction over the frequency band is constructed, and an improved particle swarm optimization algorithm is employed for parameter optimization. The results demonstrate that the optimized BNES can effectively suppress isolated high-amplitude response branches and significantly reduce the response of the primary system within the resonance frequency band, exhibiting superior broadband vibration mitigation performance and enhanced stability. Full article

Segmented telescopes face significant challenges in achieving high segment positioning accuracy under complex disturbances, which directly impact observational sensitivity and resolution. Conventional rigid actuators with limited bandwidth (e.g., Keck ~20 Hz) struggle to maintain control stability. Novel dual-stage actuators combining coarse and fine adjustment (e.g., voice coil motors) now achieve <8 nm precision over millimeter-level strokes. Moreover, their higher closed-loop bandwidth (e.g., TMT ~60 Hz) can ensure rapid settling without overshoot and robust suppression of high-frequency disturbances (e.g., pulsating wind and mechanical vibration). In parallel, system-level control strategies have been updated accordingly. Ground-based systems focus on real-time multimodal decoupling, while space-based systems emphasize non-contact vibration isolation and nested multi-loop control to achieve sub-arcsecond pointing stability. This review surveys the design and control strategies of damping–positioning mechanisms for segmented telescopes and discusses the key trade-offs among critical performance metrics, including resolution, stroke, and load capacity. Particular attention is given to the disturbance-sensitivity analysis and active damping techniques (up to ~50% vibration reduction) implemented in the ELT “hard” actuator approach. Future directions include cross-scale collaborative control, smart material applications, and AI-based adaptive parameter optimization, which together provide a technical pathway toward high-precision imaging in next-generation highly segmented telescopes. Full article

Permanent magnet synchronous machines (PMSMs) are widely used in high-performance drive systems. However, parasitic torque ripple remains a critical limitation, causing acoustic noise, mechanical vibration, and speed fluctuations. This study presents a compact, model-based torque control strategy for surface-mounted PMSMs (SPMSMs) that suppresses torque ripple by generating a structured current reference. Grounded in the magnetic co-energy principle, the proposed method utilizes a deterministic analytical model to compensate for cogging torque and inductance harmonics, avoiding computationally intensive iterative estimators. A primary contribution involves adapting the harmonic injection profile to varying loads and magnetic saturation levels. Comprehensive finite element analysis (FEA) co-simulations demonstrate that the proposed method reduces torque ripple by approximately 87.5% and speed ripple by over 90% at 1500 RPM compared to conventional maximum torque per ampere (MTPA) strategies. Furthermore, extended dynamic analysis confirms superior robustness during start-up, transients, and low-speed operation (100 RPM), maintaining high control authority even under deep magnetic saturation (2.0 p.u.). Performance evaluations verify that this significant enhancement in torque quality is achieved with a negligible increase in total power losses (~2.1%), presenting a computationally feasible solution for industrial embedded platforms. Full article

Primary biliary cholangitis (PBC) is an autoimmune-mediated disease characterized by chronic, non-suppurative, small-duct lymphocytic cholangitis. The prognosis largely depends on early disease recognition and treatment. Suboptimal response to first-line therapy (ursodeoxycholic acid) is associated with risk for disease progression. Reliable biomarkers are also required to enhance risk stratification. The traditional gold standard for assessing fibrosis is liver biopsy, but it is invasive and unsuitable for serial evaluations. Hence, trends are towards non-invasive surrogate biomarkers (blood-based and imaging biomarkers respectively) which have a much better safety profile. Blood-based biomarkers include: (i) Fibrosis-4 [Fib-4], (ii) Aspartate Aminotransferase to Platelet Ratio Index [APRI], (iii) Enhanced Liver Fibrosis score [ELF], and (iv) total bile acid to platelet ratio [TPR]. They show much potential but are not particularly sensitive tests. Ultrasound-based imaging biomarkers are increasingly being utilized for liver stiffness measurement (LSM), with vibration-controlled transient elastography (VCTE) emerging as the preferred technique. However, despite its growing popularity, VCTE is limited by technical issues. Hence, currently, none of the non-invasive tests fulfill the prerequisites to be the new gold standard as defined by the FDA. Nonetheless, there may be value to combining LSM with various serum biomarkers such as Fib-4, APRI, as aforementioned. The hope is to create nomograms for predicting liver-related events and decision tree algorithms. Newer studies are investigating microbiota in the gut-liver axis, biomolecules such as nanovesicles/nanofibers, and metabolic reprogramming as it pertains to e.g., proteomics and lipidomics. These approaches hold much promise, and if validated, could significantly change the management of PBC. Full article

To address the limitations regarding poor adaptability to complex forest environments as well as high installation and operational costs in existing mountain transportation equipment, a modular cable-type equipment for moso bamboo transportation was designed based on the terrain characteristics of steep bamboo forests and specific transportation requirements. This study first presents the overall structure and working principle of the transportation equipment. Next, a theoretical analysis and component selection were conducted for critical parts such as the wire rope, supporting components, wire-rope-driven devices, and hydraulic systems. Then, the static characteristics of the supporting components and the vibration characteristics of the wire rope were simulated and analyzed. Finally, performance testing of the equipment was conducted, focusing on transportation productivity and machine utilization. The results showed that the maximum deformation of the supporting components was 1.75 mm, occurring at the lower roller–rail contact region. During unloading, the first-order principal vibration amplitude of the wire rope had the greatest impact at the mid-span position, with a value of 0.27 m. The vibration frequency of the wire rope during operation is influenced by the its initial tension, load mass, and attachment distance, with the first-order frequency range approximately between 0.85 and 3.90 Hz. Within this frequency range, the bouncing excitation caused by moso bamboo does not induce resonance in the wire rope. The transportation productivity of the equipment was 2.61 tons per hour, with the machine utilization rate exceeding 95%. This study indicates that the designed cable-type equipment effectively meets the requirements for moso bamboo transportation in complex forest environments. Full article

Vibrations of thin sheet-metal parts during robotic manipulation on a production line create a number of serious challenges for production process planning. Modeling the behavior of an elastic plate or shell as a function of the robot manipulator trajectory is typically performed using the finite element method (FEM) and requires significant computational effort. The time factor remains a key limitation for integrating operations involving flexible parts into the virtual commissioning process. In this work, a methodology is proposed that enables accurate real-time reproduction of the behavior of an elastic part during linear robotic manipulation. The approach is based on modeling the response of an elastic part to a prescribed base excitation using the FEM and on the development of a reduced model compliant with the FMI/FMU standard. This reduced model computes, in real time, the convolution of the precomputed base response with the acceleration profile corresponding to the robot TCP trajectory. This makes it possible to determine the total cycle duration, which consists of the part transfer time and the time required for vibration decay at the end of the trajectory down to an acceptable threshold, as well as to perform collision checking while accounting for the deformation of the flexible part. As a result, operations involving elastic parts can be integrated into the virtual commissioning process. Full article

This study proposes an exploratory framework for identifying stress locations during travel with low-speed mobility devices (LMDs), such as electric wheelchairs. In this framework, stress factors perceived during LMD travel were identified through a post-ride questionnaire, and the travel route was divided into 100 m segments to enable location-specific stress evaluation. The identified factors were quantified using environmental data to construct an environment-based stress estimation index. Based on these quantified factors, a Composite Stress Score (CSS) was calculated to estimate stress levels along the route. Experiments with healthy adult participants were conducted to examine the feasibility of the proposed method. The results identified poor road surface conditions and vibrations, encounters with other road users, and narrow sidewalks as key stress factors during LMD travel. To examine whether the proposed method captures stress-related responses, correlations between CSS-based stress estimates and heart rate variability (HRV) indices were analyzed. The results showed that CSS calculated from poor road surface/vibrations, encounters with other road users, and narrow sidewalks exhibited moderate negative correlations with SDNN, suggesting that higher CSS values may correspond to increased physiological stress responses. These findings provide preliminary support for the exploratory feasibility of estimating potential stress locations during LMD travel using environmental data. However, the generalizability of the results is limited due to the specific experimental route and the use of healthy adult participants. Full article

Ride comfort is a critical issue in vehicle dynamics, as excessive vibrations adversely affect passenger comfort and human health. This paper presents a comparative performance analysis of a passive suspension system, fuzzy logic control (FLC), and a newly designed adaptive robust control (ARC) strategy applied to a nonlinear quarter-car suspension–seat–driver model. The primary objective is to improve ride comfort while maintaining vibration levels within accepted health criteria. First, the nonlinear dynamic model of the suspension–seat–driver system is established. The FLC structure and rule base are determined based on heuristic knowledge. Passive and FLC-based systems, while effective to some extent, suffer from limited adaptability to external disturbances and modeling uncertainties, slower convergence, and suboptimal vibration attenuation. The main contribution of this study is the design and implementation of a novel adaptive robust controller that effectively handles modeling uncertainties, external disturbances, and parameter variations. Different controller placement approaches within the system are also investigated. Numerical simulations are conducted under identical operating conditions for the uncontrolled system and all control strategies. The results demonstrate that although the FLC improves ride comfort compared to the passive system, the proposed ARC achieves the best overall performance, providing superior vibration attenuation, faster convergence, and enhanced robustness for nonlinear vehicle suspension systems. Quantitatively, the ARC reduces head acceleration RMS from 0.1693 m/s² (passive) and 0.1422 m/s² (FLC) to 0.0705 m/s², and upper torso RMS from 0.1689 m/s² (passive) and 0.1417 m/s² (FLC) to 0.0703 m/s², corresponding to approximately 58% reduction relative to passive and 50% improvement over FLC. Full article

This article presents a mathematical analysis of the geometric continuity of industrial robot trajectories, highlighting the influence of continuity conditions on velocity, acceleration, and vibration profiles. This study proposes an original approach: isolating geometric continuity as an independent parameter and the comparative evaluation of the continuity levels C¹, C², and C³ using a series of pre-computed kinematic metrics: vibration energy, acceleration variability, and trajectory curvature stability. The methodology is validated by numerical simulations performed with RoboDK for trajectory generation and by post-processing in Python for metric evaluation. The results indicate that C¹ trajectories exhibit discontinuities in the higher-order derivatives, which lead to undesirable kinematic behaviors, while C² continuity represents the minimum requirement for an acceptable, stable motion in industrial applications. Higher-order continuity, C³, brings greater regularity to the trajectories, but the practical advantages are limited and relatively insignificant for standard industrial applications. Full article

The runner of a pump turbine features a relatively flat structural configuration. The clearance cavities formed between the upper crown, lower band, and surrounding stationary components play a critical role in its dynamic behavior and operational stability. Consequently, a detailed modal analysis of the runner is essential to ensure safe and stable operation. In this study, an acoustic–structure coupling method is adopted to investigate the wet modal characteristics of the pump-turbine runner, explicitly accounting for the added mass effect induced by the fluid in the external flow passages. By systematically varying the geometric parameters of the upper crown clearance cavity, the influence of seal clearance dimensions on the runner’s modal characteristics is examined. The results demonstrate that the radial clearance and the axial height of the seal cavity are the most influential parameters, reducing natural frequencies by up to 15.85% and 16.93%, respectively. The pitch of the seal teeth shows a secondary yet notable effect, inducing a frequency variation of 13.73%. In contrast, local labyrinth seal parameters, such as the number of teeth and tooth width, have a comparatively limited effect. This study provides practical guidance for vibration risk prediction, anti-resonance design, and operational stability assessment of high-head, large-capacity turbine runners by revealing the quantitative relationship between geometric parameters and modal frequencies. Full article

Background: Endometriosis is a major cause of female infertility. It significantly impacts oocyte quality and embryonic development. The condition’s pathophysiological mechanisms are multifactorial. However, they are believed to be reflected in the biochemical composition of follicular fluid (FF). FF is the immediate microenvironment of the developing oocyte hence its relevance. Conventional analytical methods provide only a limited view of this complex biofluid. This underlies the need for holistic profiling techniques. Objective: This narrative review synthesizes current knowledge on the potential of Fourier-Transform Infrared (FTIR) and Raman spectroscopy. The two are scrutinized as label-free, non-destructive tools for analyzing FF in the context of endometriosis. As such, the aim is to bridge the understanding of the disease’s impact on the follicular niche with the analytical power of these spectroscopic techniques, ultimately highlighting a critical research gap, while critically evaluating the translational pathway required to bring these techniques from research laboratories into routine clinical IVF practice. This includes assessment of practical feasibility, cost-effectiveness, turnaround time, standardization requirements, and comparison with existing clinical biomarkers. Methods: We outline the fundamental principles of FTIR and Raman spectroscopy and their complementary strengths. The review then consolidates evidence from proteomic and metabolomic studies demonstrating FF alterations in endometriosis. We also showcase the successful application of vibrational spectroscopy in other reproductive diagnostics. This synthesis is vital to identifying a specific unmet need in the field. Conclusions: Despite the known importance of FF and the proven capability of FTIR and Raman spectroscopy in related areas, there is a striking lack of studies applying these techniques directly to the FF of women with endometriosis. This review concludes by framing this void as a pivotal research opportunity. In doing so, it presents a direct rationale and methodological framework for a future study designed to characterize the unique spectral fingerprints of endometriosis in FF, with the goal of uncovering novel biomarkers and pathophysiological insights. Full article

Ball balancers are nonlinear, electromechanical, multivariable, open-loop unstable systems widely used in research laboratories, aerospace, military, and automotive industries to evaluate control mechanism effectiveness. The inherent difficulty in precisely managing ball position, combined with actuator saturation and system sensitivity to disturbances, makes trajectory tracking a persistent challenge. Conventional controllers often exhibit oscillatory responses with steady-state errors exceeding acceptable limits. Sliding mode control (SMC) offers robustness against model uncertainties; however, chattering finite-frequency, finite-amplitude oscillations near the sliding surface caused by switching imperfections, time delays, and actuator dynamics remain a significant limitation. This study addresses chattering through explicit vibration model compensation integrated into the SMC design for a 2-DOF ball balancer system using a visual servoing approach. A double-loop control architecture is implemented, where the inner loop handles servo angular position control and the outer loop manages ball position tracking through visual servoing feedback. The sliding mode controller is designed with a power rate reaching law, synthesizing two control laws: one with explicit vibration model compensation incorporating damping and stiffness terms, and one without. Experimental validation confirmed that SMC with compensation achieved significantly reduced steady-state error (0.034 mm vs. 0.386 mm) and lower overshoot (3.95% vs. 13.81%) compared to the uncompensated variant, with chattering amplitude reduced by approximately 72%. Full article

As critical components of wind energy systems, the structural integrity of wind turbine blades is directly tied to the operational safety and economic performance of wind turbines. With blade designs trending toward larger and more flexible structures and operating environments becoming increasingly harsh, maintenance strategies must urgently shift from reactive approaches to predictive maintenance paradigms. From an engineering application perspective, this study conducts a systematic and critical review of non-destructive testing (NDT) and structural health monitoring (SHM) technologies for wind turbine blades. Drawing on the literature published over the past decade, we examine the field applicability, limitations, and engineering challenges of core NDT techniques—including vision-based methods, acoustic approaches, vibration analysis, ultrasound, and infrared thermography. Particular emphasis is placed on the integration of data-driven approaches with engineering practice, evaluating the role of machine learning in fault classification and anomaly diagnosis, as well as the contributions of deep learning to automated defect detection in image and signal data. Moreover, this paper critically discusses the growing use of robotic inspection platforms, such as unmanned aerial vehicles and climbing robots, as multi-sensor carriers enabling rapid and comprehensive blade assessment. By comparatively analyzing detection performance, cost, and automation levels across technologies, we identify key engineering barriers, including environmental noise robustness, signal attenuation within complex blade structures, and the persistent gap between laboratory methods and field deployment. Finally, we outline forward-looking research directions, encompassing multi-modal sensor fusion, edge computing for real-time diagnostics, and the development of standardized SHM systems aimed at supporting full lifecycle blade management. Full article

Rolling bearings exhibit nonlinear and non-stationary fault signals under complex working conditions, rendering single-modal representation insufficient for accurate diagnosis. To address this limitation, this paper proposes a novel parallel multimodal fusion fault diagnosis model based on a Gated Recurrent Unit (GRU), a Residual Network (ResNet), and a Convolutional Block Attention Module (CBAM). First, a systematic multimodal representation selection framework is introduced, identifying the Markov Transition Field (MTF) as the optimal two-dimensional (2D) image modality due to its superior texture clarity and noise resistance compared to other methods. Second, parallel dual-branch architecture is designed to simultaneously process heterogeneous data. The 1D-GRU branch captures long-range temporal dependencies directly from raw vibration signals, while the 2D ResNet-CBAM branch extracts deep spatial features from the MTF images, adaptively focusing on key fault regions. These heterogeneous features are then fused through concatenation to retain complementary diagnostic information. Experimental validation on the Case Western Reserve University (CWRU) dataset demonstrates that the proposed model achieves a 99.57% accuracy in a 10-classification task. Furthermore, it exhibits significant parameter efficiency and outstanding robustness, with the accuracy decreasing by no more than 1.2% under noise interference and cross-load scenarios, comprehensively outperforming existing single-modal and advanced fusion methods. Full article