Search Results (3,972)

Microelectromechanical systems (MEMSs) are increasingly used for both industrial and consumer applications. To improve the accuracy and efficiency of MEMS fabrication and to overcome the limitations of conventional contactless positioning systems, this study introduces a novel positioning concept that combines ultrasonic levitation with electromagnetic actuation. Squeeze-film effects generated by high-frequency ultrasonic transducers enable levitation, while fast-response reluctance forces from electromagnets govern the positioning dynamics without requiring bulky mounting frames. The focus of this paper is on proposing a novel double-acting ultrasonic transducer with a Gaussian profile horn, ensuring an approximately uniform vibration distribution and increased levitation force. The double-acting design enables levitation on both surfaces, simplifying the mounting and thermal compensation of the transducer’s expansion while reducing interactions among transducers. A model-based control strategy ensures resonant operation and constant vibration amplitude. Experiments demonstrate levitation forces up to 343 N, with a total levitation height of 25 µm, resulting from two levitation air gaps. Comprehensive performance characterization validates the feasibility of this transducer design for integration into the proposed positioning system. Full article

Infrared (IR) thermal sensors on CMOS-SOI-MEMS platforms enable scalable, low-cost thermal imaging but require optimized optical, thermal, and mechanical performance. This paper presents a multiphysics modeling framework to study the integration of Metasurface absorbers into a Thermal CMOS-SOI-MEMS IR sensor. Using finite-difference time-domain (FDTD) simulations, we demonstrate near-unity absorption at targeted wavelengths (e.g., 4.26 µm for CO₂ sensing, 10 µm for thermal imaging) compared to conventional absorbers. The absorbed power, calculated from blackbody irradiance, drives thermal finite element analysis (FEA), confirming high thermal isolation and maximized temperature rise (ΔT) while quantifying the thermal time constant’s sensitivity to Metasurface mass. An analytical RC circuit model, validated against 3D FEA, accurately captures thermal dynamics for rapid design iterations. Mechanical modal and harmonic analyses verify structural integrity, with natural frequencies above 20 kHz, ensuring resilience against mechanical resonances and environmental vibrations. This holistic framework quantifies trade-offs between optical efficiency, thermal responsivity, and mechanical stability, providing a predictive tool for designing high-performance, uncooled IR sensors compatible with CMOS processes. Full article

High-frequency sensitivity to external acceleration is crucial for improving the accuracy of MEMS resonant accelerometers. This study proposes utilizing shape optimization of microlevers and resonators to improve sensitivity. Initially, an optimization model for microlevers is established, considering the arm’s shape and the dimensions of the pivots, outputs, inputs, and supported beams. Secondly, shape optimization for the resonant beam of the tuning fork resonators is implemented, utilizing a bi-objective function to maintain the fundamental frequency. Finally, the genetic algorithm is employed in both optimizations to search for the global optimal solution. The microlever optimization achieves a high sensitivity of 286.9 Hz/g, and the final trapezoidal arm shape offers the advantage of accommodating a larger proof mass within a given die area. Meanwhile, the resonator optimization improves the sensitivity to axial inertial force from 727 Hz/mN to 1338.5 Hz/mN while keeping the fundamental frequency at approximately 20,000 Hz. Integrating the optimized microlevers and resonators yields a very high sensitivity of 480.2 Hz/g, and the sensitivity per proof mass area is significantly higher than that reported in previous studies. Full article

The growing demand for wideband electronically scanned arrays (ESAs) in next-generation radar, satellite, and 5G/6G systems has renewed interest in true time delay units (TDUs) to overcome the limitations of phase-based beamforming. In parallel, recent advances in the commercial availability and reliability of packaged RF MEMS switches have enabled practical hardware implementations once considered infeasible. This paper presents the design, fabrication, and experimental validation of a broadband, 4-bit switched-line TDU using only off-the-shelf components and standard PCB processes. The unit operates from 0.4 to 6 GHz, with a total delay range of 0–413 ps, achieving an average insertion loss of 1.5 dB and delay error below 18.4 ps, resulting in a figure of merit (FOM) of 152.8 ps/dB. Measured results are reported alongside a refined switch/termination model that aligns simulations with measurements. This is among the first reported demonstrations of a complete RF MEMS-based TDU implemented entirely with commercially available components in a standard PCB-integrated implementation. These results demonstrate a practical pathway toward scalable MEMS-based TDUs for deployment in advanced beamforming systems. Full article

Background and Purpose: Adherence to oral anticancer therapy correlates with outcome. Lenalidomide (LEN) is an oral mainstay treatment for multiple myeloma (MM), administered in 21-day/7-day (on/off) cycles. Data on LEN adherence is limited. Electronic monitoring (EM) represents the most reliable adherence assessment method. Experimental Approach: We conducted a prospective observational study using electronic medication event monitoring (MEMS^®) in lenalidomide-naïve multiple myeloma patients to quantify adherence during on/off cycles and identify patterns of non-adherence in real-world practice. On and off cycles were determined semi-automatically. Implementation adherence was calculated as the proportion of prescribed drug taken, during each on cycle and across all on cycles. Daily adherence predictors were analyzed using logistic regression with generalized estimating equations. Key Results: Eighty-five patients were included. Median age was 68 years, 66% received LEN as a second-line treatment, 75% of patients perfectly adhered to the recommended 21/7-day on/off cycle. Median implementation adherence was 100%. Only 4% of patients had a proportion of doses taken below 90%. All doses were taken by 51% of patients, while 9% missed ≥4 doses. Among the 13 predictors investigated, only age under 80 and participation in a support group were statistically significant. Conclusions: this novel assessment of LEN adherence in MM patients demonstrated high implementation adherence and cycle duration compliance. Full article

Due to the limitations of traditional micropumps in terms of miniaturization and integration, ferrofluid micropumps, as emerging microfluidic driving devices, exhibit significant application potential due to their unique pumping mechanism. Research on ferrofluid micropumps can advance micro/nano technology, meet biomedical needs, and facilitate micro-electro-mechanical system (MEMS) integration. As traditional structural improvement methods struggle to meet increasingly stringent application conditions, under the action of the motion and mechanism of magnetic fluids, a new method of using neodymium magnetic ball plugs instead of traditional magnetic fluid plungers has been developed, aiming to enhance the pumping performance. In this study, the influence of the magnetic field (MF) generated by permanent magnets (PM) on the magnetic properties inside the micropump cavity was first determined. Furthermore, it was revealed in this research that the neodymium magnetic ball plug enhances the pumping flow rate and maximum pumping height of the ferrofluid plug and the pumping stability of the neodymium magnetic ball plug ferrofluid micropump is significantly improved. Additionally, the rotational speed (Rev) of the dynamic neodymium magnetic ball type magnetic fluid plug driven by the motor and the magnetic strength created by the PM are the main aspects influencing the result in this experiment. Full article

Recent advancements in cyber threats have led to increasingly sophisticated attack methods that evade traditional malware detection systems. In-memory malware, a particularly challenging variant, operates by modifying volatile memory, leaving minimal traces on secondary storage. This paper presents an in-depth analysis of in-memory malware characteristics, behavior, and evasion strategies. We propose “MemCatcher”, a novel detection algorithm that integrates real-time system activity monitoring and memory analysis to effectively identify these threats from the Windows 10 system. Experimental validation using real-world and synthetic in-memory malware samples demonstrates the effectiveness of our approach. Additionally, we analyze evasion tactics using “Volatility3” and “PEview”, providing insights into countermeasures. Future work will focus on enhancing in-memory malware detection using “Processor-in-Memory (PIM) hardware”. Full article

Indeterminate serological results for SARS-CoV-2 antibodies create diagnostic uncertainty, requiring repeat testing after 14–21 days to establish seroconversion. This study evaluated whether direct viral detection methods could provide immediate diagnostic information in serum samples with indeterminate antibody results. We analyzed 163 serum samples from clinically healthy individuals collected during March–December 2020 in Bulgaria. Samples were categorized by screening ELISA (IgA/M/G) as positive (n = 69), negative (n = 47), or indeterminate (n = 47). All samples underwent quantitative IgG ELISA, rapid antibody tests, rapid antigen detection (viral nucleoprotein), and RT-nested PCR. Among samples with indeterminate antibody results, 27.7% (13/47; 95% CI: 15.6–42.6%) tested positive by rapid antigen detection and 12.8% (6/47; 95% CI: 4.8–25.7%) by RT-PCR. All PCR-positive samples were also antigen-positive (Cohen’s κ = 0.69). Viral detection rates showed a gradient: antibody-positive samples 30.4% (antigen) and 16.4% (PCR), indeterminate samples 27.7% and 12.8%, antibody-negative samples 10.6% and 4.3%, respectively. The algorithm we proposed and the diagnostic methods used enable the application of certain approaches to differentiate infected from uninfected clinically healthy people, in case of intermediate antibody results. Direct viral detection identified evidence of potential SARS-CoV-2 infection in more than one-quarter of sera with indeterminate antibody results. These findings suggest immediate viral detection testing may complement standard serological approaches, though clinical validation through longitudinal studies is essential before routine implementation. Full article

This paper provides a comprehensive review of recent advancements in radio-frequency (RF) multifunctional components with integrated filtering characteristics, including tunable filtering attenuators, filtering power dividers, filtering couplers, and filtering Butler matrices, all of which play critical roles in wireless communication systems. With the increasing demand for miniaturization, integration, and low-loss performance in RF front-ends, multifunctional components with filtering characteristics have become essential. This review first introduces tunable attenuators and filtering attenuators based on various technologies such as PIN diodes, graphene-based structures, and RF-MEMS switches, and also analyzes their advantages, limitations, and performance. Then, we discuss filtering power dividers developed from Wilkinson structures, three-line coupled structures, resonator-based coupling matrix methods, and SSPP-waveguide hybrids. Furthermore, filtering couplers and filtering Butler matrices are reviewed, highlighting their capability to simultaneously achieve amplitude and phase control, making them suitable for multi-beam antenna feeding networks. Finally, a brief conclusion is summarized. Future research directions, such as hybrid technologies, novel materials, broadband and multi-band designs, and antenna-matrix co-design, are suggested to further enhance the performance and practicality of multifunctional RF components for next-generation wireless communication systems. Full article

The accurate acquisition and real-time calculation of the attitude angle of precision firefighting extinguishing projectiles are essential for ensuring stable flight and precise extinguishing agent release. However, measuring the roll attitude angle in such projectiles is challenging due to their highly dynamic nature and environmental disturbances such as fire smoke, high temperature, and electromagnetic interference. Traditional methods for measuring attitude angles rely on multi-sensor fusion schemes, which suffer from complex structure and high cost. This paper proposes a single-gyro attitude calculation method based on micro-electromechanical inertial measurement units (MIMUs). This method integrates Fourier transform time-frequency analysis with a second-order Infinite Impulse Response (IIR) bandpass filtering algorithm optimized by dynamic coefficients. Unlike conventional fixed-coefficient filters, the proposed algorithm adaptively updates filter parameters according to instantaneous roll angular velocity, thereby maintaining tracking capability under time-varying conditions. This theoretical contribution provides a general framework for adaptive frequency-tracking filtering, beyond the specific engineering case of firefighting projectiles. Through joint time-frequency domain processing, it achieves high-precision dynamic decoupling of the roll angle, eliminating the dependency on external sensors (e.g., radar/GPS) inherent in conventional systems. This approach drastically reduces system complexity and provides key technical support for low-cost and high-reliability firefighting projectile attitude control. The research contributes to enhancing the effectiveness of urban firefighting, forest fire suppression, and public safety emergency response. Full article

Vibration-based predictive maintenance is an essential element of reliability engineering for modern automotive powertrains including internal combustion engines, hybrids, and battery-electric platforms. This review synthesizes advances in sensing, signal processing, and artificial intelligence that convert raw vibration into diagnostics and prognostics. It characterizes vibration signatures unique to engines, transmissions, e-axles, and power electronics, emphasizing order analysis, demodulation, and time–frequency methods that extract weak, non-stationary fault content under real driving conditions. It surveys data acquisition, piezoelectric and MEMS accelerometry, edge-resident preprocessing, and fleet telemetry, and details feature engineering pipelines with classical machine learning and deep architectures for fault detection and remaining useful life prediction. In contrast to earlier reviews focused mainly on stationary industrial systems, this review unifies vibration analysis across combustion, hybrid, and electric vehicles and connects physics-based preprocessing to scalable edge and cloud implementations. Case studies show that this integrated perspective enables practical deployment, where physics-guided preprocessing with lightweight models supports robust on-vehicle inference, while cloud-based learning provides cross-fleet generalization and model governance. Open challenges include disentangling overlapping sources in compact e-axles, coping with domain and concept drift from duty cycles, software updates, and aging, addressing data scarcity through augmentation, transfer, and few-shot learning, integrating digital twins and multimodal fusion of vibration, current, thermal, and acoustic data, and deploying scalable cloud and edge AI with transparent governance. By emphasizing inverter-aware analysis, drift management, and benchmark standardization, this review uniquely positions vibration-based predictive maintenance as a foundation for next-generation vehicle reliability. Full article

This paper presents a Vibration-based Track Anomaly Detection (VTAD) system designed for real-time monitoring of urban tram infrastructure. The novelty of VTAD is that it converts existing public transport vehicles into distributed mobile sensor platforms, eliminating the need for specialized diagnostic trains. The system integrates low-cost micro-electro-mechanical system (MEMS) accelerometers, Global Positioning System (GPS) modules, and Espressif 32-bit microcontrollers (ESP32) with wireless data transmission via Message Queuing Telemetry Transport (MQTT), enabling scalable and continuous condition monitoring. A stringent ±6σ statistical threshold was applied to vertical vibration signals, minimizing false alarms while preserving sensitivity to critical faults. Field tests conducted on multiple tram routes in Zagreb, Croatia, confirmed that the VTAD system can reliably detect and locate anomalies with meter-level accuracy, validated by repeated measurements. These results show that VTAD provides a cost-effective, scalable, and operationally validated predictive maintenance solution that supports integration into intelligent transportation systems and smart city infrastructure. Full article

Aiming at the challenge of balancing the accuracy and cost of the initial state calibration of traditional MEMS inertial navigation systems, as well as the current situation of the lack of high-precision three-axis turntables in engineering practice, this paper proposes a practical and innovative systematic error calibration and compensation scheme, which effectively suppresses the deterministic errors of MEMS-INS and enhances its applicability in high-precision and long-duration tasks. By analyzing the coordinate transformation characteristics of the MEMS-INS solution process under small-angle disturbances, a deterministic error model based on the device’s zero bias, scale factor, and cross-coupling errors is constructed. A twelve-position dual-axis calibration method, combined with a high-precision orthogonal fixture, is designed to excite errors on a dual-axis turntable, converting originally unobservable error terms into observable periodic signals. Experimental results show that the installation error calibration accuracy reaches 0.03°, an improvement of about 25% compared to the traditional dual-axis method, breaking through the limitations of dual-axis turntables in cross-coupling error calibration, achieving an initial error ≤ 1 μrad, and reducing the navigation error by 90% within one hour. This method eliminates reliance on expensive three-axis turntables while enabling multi-error calibration, addressing the cost–accuracy trade-off in engineering applications. Full article

The rapid advancement of micro/nano-electromechanical systems (MEMS/NEMS) and precision manufacturing has fundamentally challenged traditional friction theories at the nanoscale. Classical continuum models fail to capture energy dissipation mechanisms at the atomic level, which are influenced by interfacial phenomena such as electron transfer, charge redistribution, and energy level realignment. Density functional theory (DFT), renowned for its accurate description of ground-state properties in many-electron systems, has emerged as a key tool for uncovering quantized friction mechanisms. By quantifying potential energy surface (PES) fluctuations, the evolution of interfacial charge density, and dynamic electronic band structures, DFT establishes a universal correlation between frictional dissipation and electronic behavior, transcending the limitations of conventional models in explaining stick–slip motion, superlubricity, and non-Amonton effects. Research breakthroughs in the application of DFT include characterizing frictional chemical potentials, designing heterojunction-based superlubricity, elucidating strain/load modulation mechanisms, and resolving electronic energy dissipation pathways. However, these advances remain scattered across interdisciplinary studies. This article systematically summarizes methodological innovations and cutting-edge applications of DFT in computational tribology, with the aim of constructing a unified framework for carrying out the “electronic structure–energy dissipation–frictional response” predictions. It provides a state of the art of using DFT to help design high-performance lubricants and actively control interfacial friction. Full article

This research highlights the issue that large amount of sweat generated by metabolism cannot be discharged from the internal environment of traditional fire suits when firefighters are intensively operating in high-temperature environments. This is highly prone to bacterial growth, which brings much harm to their health. Therefore, this study aims to present a new fire-retardant fabric with both antibacterial and high hygroscopic properties. Blended fibers were used including aramid 1313 fibers with excellent flame retardancy and flame-retardant viscose fibers. By uniformly embedding antibacterial nanofibers into the microfiber aggregates and controlling the adhesion behavior at the cross-scale interfaces of micro–nano fibers, the fire-retardant yarns were endowed with both antibacterial and moisture-transporting properties. The bacterial inhibition rate was calculated by comparing colonies cultured on EF fabric versus NF fabric. Additionally, the antibacterial and moisture-wicking properties of the fabrics were verified through tests such as placing the fabrics vertically in liquid to measure the height of absorbed moisture. This prepared functionally integrated fabric has excellent antibacterial properties even after 50 washing cycles. Its antibacterial rate against Escherichia coli and Staphylococcus aureus kept a preferred result of 99%. Its moisture-transporting performance has also been significantly improved. Based on the above, this study has not only successfully developed a flame-retardant fabric with high antibacterial and moisture-wicking properties, but more importantly, the method demonstrates a degree of universal applicability. Full article