Search Results (5,469)

This work analyzes the hydrodynamic behavior of a magnetorheological valve, considering the microscopic fluid characteristics to generate a damper force. The magnetorheological fluid is composed of ferromagnetic particles dispersed in a non-magnetic carrier fluid, whose mechanical resistance depends on the magnetic field intensity. In the absence of a magnetic field, the magnetorheological fluid behaves as a liquid whose viscosity depends on the particle volume fraction. Conversely, the presence of a magnetic field generates particle chain-like structures that inhibit fluid motion, thereby regulating flow in the control valve. The mathematical model employs the continuity and momentum equations, the Bingham model, and the boundary conditions at the solid–liquid interfaces to determine the flow field. The results show the fluid hydrodynamic response under different flow conditions depending on dimensionless parameters such as the pressure gradient, the field-independent viscosity, the yield stress, the particle volume fraction, the Bingham number, the Mason number, and the critical Mason number. For a pressure gradient of $\Gamma =-10$, the flow rate inside the valve (with particle volume fraction $\varphi =0.2$) results in ${\overline{Q}}_{T,x}=0.34$, $0.06$, and 0 when the magnetic field is 80, 120, and 160 kA m⁻¹, respectively. Likewise, when the magnetic field increases from 80 to 160 kA m⁻¹, the damping capacity increases by $88\%$ when $\varphi =0.2$ and $128\%$ when $\varphi =0.3$ compared to the Newtonian viscous damping. This work contributes to our understanding of semi-active damping devices for flow control. Full article

This study presents a novel energy harvesting device that combines piezoelectric and electromagnetic transduction to extract energy from fluid flow within pipelines to supply power to wireless sensor nodes for the digital transformation of pipeline networks. The proposed harvester consisted of a permanent magnet, an unimorph circular piezoelectric plate, an adjustable housing, two wound coils, and a coil holder. In laboratory tests, the harvester demonstrated an ability to produce 831.7 µW of AC power and 680 µW of DC power at a flow pressure of 2.90 kPa and a flow rate of 11.083 L/s. The energy harvester charged a power backup from 1.01 V to 4.49 V in a time duration of 120 min. Additionally, a low-power wireless system for monitoring pipeline pressure was developed and integrated with this energy harvesting system. By incorporating this technology into the digitization of pipeline systems, continuous power generation is possible, ensuring the reliable and autonomous operation of sensors for real-time data collection and monitoring of the pipeline network. The hybrid flow energy harvester surpasses both earlier standalone electromagnetic and piezoelectric flow energy harvesters. Full article

Aluminum nitride (AlN) is a wide-bandgap semiconductor (6.2 eV) with a broad transparency window spanning from the ultraviolet (UV) to the mid-infrared (MIR) wavelength region, making it a promising material for integrated photonics. In this work, AlN thin films using reactive RF sputtering are deposited, followed by annealing at 600 °C in a nitrogen atmosphere to reduce slab waveguide propagation losses. After annealing, the measured loss is 0.84 dB/cm at 978 nm, determined using the prism coupling method. A complete microfabrication process flow is then developed for the realization of optical channel waveguides. A key challenge in the processing of AlN is its susceptibility to oxidation when exposed to water or oxygen plasma, which significantly impacts device performance. The process is validated through the fabrication of microring resonators (MRRs), used to characterize the propagation losses of the AlN channel waveguides. The fabricated MRRs exhibit a quality factor of 12,000, corresponding to a propagation loss of 4.4 dB/cm at 1510–1515 nm. The dominant loss mechanisms are identified, and strategies for further process optimization are proposed. 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

Maintaining an effective facial seal is critical for the performance of tight-fitting industrial respirators used in high-risk sectors such as mining, manufacturing, and construction. Traditional fit verification methods—Qualitative Fit Testing (QLFT) and Quantitative Fit Testing (QNFT)—are limited to periodic assessments and cannot detect fit degradation during active use. This study presents a real-time fit detection system based on embedded breath sensors and machine learning algorithms. A compact sensor module inside the respirator continuously measures pressure, temperature, and humidity, transmitting data via Bluetooth Low Energy (BLE) to a smartphone for on-device inference. This system functions as a multimodal biosensor: intra-mask pressure tracks flow-driven mechanical dynamics, while temperature and humidity capture the thermal–hygrometric signature of exhaled breath. Their cycle-synchronous patterns provide an indirect yet reliable readout of respirator–face sealing in real time. Data were collected from 20 healthy volunteers under fit and misfit conditions using OSHA-standardized procedures, generating over 10,000 labeled breathing cycles. Statistical features extracted from segmented signals were used to train Random Forest, Support Vector Machine (SVM), and XGBoost classifiers. Model development and validation were conducted using variable-size sliding windows depending on the person’s breathing cycles, k-fold cross-validation, and leave-one-subject-out (LOSO) evaluation. The best-performing models achieved F1 scores approaching or exceeding 95%. This approach enables continuous, non-invasive fit monitoring and real-time alerts during work shifts. Unlike conventional techniques, the system relies on internal physiological signals rather than external particle measurements, providing a scalable, cost-effective, and field-deployable solution to enhance occupational safety and regulatory compliance. Full article

The ring-assisted Mach–Zehnder interferometer (RA-MZI) has high sensitivity and fast optical response time, and it has been used as a label-free nanophotonic biosensor. Most RA-MZI-based biosensors, however, require chemical modification of the ring surface to immobilize biomolecules that can interact with target molecules for sensing. Here, we report a novel microfluidic-integrated RA-MZI (μFRA-MZI) where a microfluidic channel was fabricated right above the photonic ring resonator. μFRA-MZI allows for direct sample delivery to the RA-MZI without chemical modification of the ring surface and measures shifts in the resonance wavelength induced by the presence of target molecules, enabling label-free detection. In order to optimize the sensitivity of μFRA-MZI, seven devices were fabricated with varied design parameters, including the gap distance between the ring and the bus waveguide (G_ring), the length of the multi-mode interferometer (L_MMI), and the length of the directional coupler (L_DC). Photonic characterization showed that the device with G_ring = 1.2 μm, L_MMI = 15.5 μm, and L_DC = 13.5 μm exhibited the highest extinction ratio (ER) compared to the other six devices, consistent with the simulation-optimized design. Testing with NaCl solutions of varying concentrations yielded a bulk sensitivity of 11.48 nm/refractive index unit (RIU) and an ER of 0.41. With the potential to further improve the device’s sensitivity and the ability to detect samples directly in flow without chemical modifications of the ring resonator, μFRA-MZI will provide a robust and effective approach for label-free biosensing. Full article

The strategic positioning of heating and cooling segments within complex non-rectangular geometries has emerged as a critical engineering challenge across multiple industries in thermal management systems for electronic components. This analysis presents a numerical inspection of buoyancy-driven convective flow and thermal transport mechnisms of nanofluids in a parallelogrammic porous geometry. A single discrete heating–cooling segment has been placed along the slanting surfaces of the geometry. The mathematical model is formulated utilizing Darcy’s law, incorporating the Tiwari and Das approach to characterize the thermophysical properties of the nanofluid. The governing model equations corresponding to the physical process are solved numerically using finite-difference-based alternating direction implicit (ADI) and successive line over-relaxation (SLOR) techniques. Computational simulations are performed for various parametric conditions, including different nanoparticle volume fractions ($\varphi =0$–$0.05$), Rayleigh numbers ($Ra={10}^1$–${10}^3$), and parallelogram geometry ($\alpha$) and sidewall ($\gamma$) tilting angles ($-{45}^{°}\le \alpha \le +{45}^{°}$ and $-{45}^{°}\le \gamma \le +{45}^{°}$), while examining the effect of discrete thermal locations. The results reveal a significant decrement in thermal transfer rates with an increasing nanoparticle concentration, particularly at higher Rayleigh numbers. The skewness of the parallelogrammic boundaries is found to substantially influence flow patterns and thermal transport characteristics compared to conventional rectangular enclosures. Further, the discrete placement of heating and cooling sources creates unique thermal plumes that modify circulation patterns within the domain. The predictions suggest profound insights for optimizing thermal management systems by employing nanofluids in non-rectangular porous configurations, with potential applications in geothermal energy extraction, electronic cooling systems, and thermal energy storage devices. Full article

Efficient real-time computer vision-based passenger flow analysis is increasingly important for the management of intelligent transportation systems and smart cities. This paper presents the design and implementation of a system for real-time object detection, tracking, and people counting in tram stations. The proposed approach integrates YOLO-based detection with a lightweight tracking module and is deployed on an NVIDIA Jetson Nano device, enabling operation under resource constraints and demonstrating the potential of edge AI. Multiple YOLO versions, from v3 to v11, were evaluated on data collected in collaboration with Metropolitano de Tenerife. Experimental results show that YOLOv5s achieves the best balance between detection accuracy and inference speed, reaching 96.85% accuracy in counting tasks. The system demonstrates the feasibility of applying edge AI to monitor passenger flow in real time, contributing to intelligent transportation and smart city initiatives. Full article

The heart is the body’s core pump. Heart failure impairs the heart’s ability to pump blood, leading to circulatory disorders. The artificial heart (blood pump) is an important mechanical circulatory support device that can partially or completely substitute cardiac pumping function, potentially improving hemodynamic performance and alleviating symptoms of heart failure. A combination of computational fluid dynamics simulation and hydraulic performance testing was used to study key parameters of the impeller, including blade count, blade wrap angle, impeller flow path, and diversion cone height. The goal was to reduce hemolysis risk and enhance pumping efficiency. Increasing the blade count raised the head, with optimal efficiency achieved at seven blades. A larger blade wrap angle decreased the head but improved efficiency. Synchronizing the flow path and diversion cone height at 4.1 mm maximized the head. Under various rotational speeds, the studied hemolysis index remained well below 0.1 g/100 L. Both experimental and simulation data were validated against each other, meeting the required error tolerances. The studied blood pump meets the design specifications. At an operating condition of 5 L/min flow rate and 2800 rpm, the pump achieves the required head and hemolysis criteria with a margin of safety. Full article

Redox flow batteries (RFBs) are an emerging class of large-scale energy storage devices, yet the commercial benchmark—vanadium redox flow batteries (VRFBs)—is highly constrained by a modest open-circuit potential (1.26 V) while posing an expensive and volatile material procurement costs. This review focuses on recent progress in diversifying redox-active species to overcome these limits, highlighting chemistries that increase overall cell voltage, energy density, and efficiency while maintaining long cycle life and safety. The study dwells deeper into manganese-based systems (e.g., Mn/Ti, Mn/V, Mn/S, M/Zn) that leverage Mn’s high positive potential while addressing Mn(III) disproportionation reactions; iron-based hybrids (Fe/Cr, Fe/Zn, Fe/Pb, Fe/V, Fe/S, Fe/Cd) that exploit the low cost, and its abundance, along with membrane and electrolyte strategies to prevent the potential issue involving crossover; cerium-anchored catholytes (Ce/Pb, V/Ce, Eu/Ce, Ce/S, Ce/Zn) that deliver high operational voltage by implementing an acid-base media, along with selective zeolite membranes; and halide systems (Zn–I, Zn–Br, Sn–Br, polysulfide–bromine/iodide) that combine fast redox kinetics and high solubility with advances such as carbon-coated membranes, bromine complexation, and ambipolar electrolytes. Across these various families of RFBs, the review highlights the modifications made to the flow-fields, membranes, and electrodes by utilizing a zero-gap serpentine flow field, sulfonated poly(ether ether ketone) (SPEEK) membranes, carbon-modified and zeolite separators, electrolyte additives to enhance the voltage (VE%), and thereby energy (EE%) efficiency, while reducing the overall system cost. These modifications to the existing RFB technology offer a promising alternative to traditional approaches, paving the way for improved performance and widespread adoption of RFB technology in large-scale grid-based energy storage solutions. Full article

Maternal health care during labor requires the continuous and reliable monitoring of analgesic procedures, yet conventional systems are often subjective, indirect, and operator-dependent. Infrared thermography (IRT) offers a promising non-invasive approach for labor epidural analgesia (LEA) monitoring, but its practical implementation is hindered by clinical and hardware limitations. This work presents a novel artificial intelligence-driven mobile platform to overcome these hurdles. The proposed solution integrates a lightweight deep learning model for semantic segmentation, a B-spline-based free-form deformation (FFD) approach for non-rigid dermatome registration, and efficient on-device inference. Our analysis identified a U-Net with a MobileNetV3 backbone as the optimal architecture, achieving a high Dice score of 0.97 and a 4.5% intersection over union (IoU) gain over heavier backbones while being 73% more parameter-efficient. The entire AI pipeline is deployed on a commercial smartphone via TensorFlow Lite, achieving an on-device inference time of approximately two seconds per image. Deployed within a user-friendly interface, our approach provides straightforward feedback to support decision making in labor management. By integrating thermal imaging with deep learning and mobile deployment, the proposed system provides a practical solution to enhance maternal care. By offering a quantitative, automated tool, this work demonstrates a viable pathway to augment or replace subjective clinical assessments with objective, data-driven monitoring, bridging the gap between advanced AI research and point-of-care practice in obstetric anesthesia. Full article

Phase-change materials (PCMs) are integral to the thermal energy storage devices used in phase-change storage air-conditioning systems, but their adoption is hindered by slow heat transfer rates and suboptimal energy storage efficiency. In this study, we design and analyze a flat-panel thermal energy storage device based on PCM, using both numerical simulations and experimental testing to evaluate performance under various operating conditions. The simulations, conducted using computational fluid dynamics (CFD) in a steady-state environment with an inlet temperature of 12 °C, demonstrate that the phase-change completion time for cooling storage is 8331 s, while the cooling release process is completed in 3883 s. The fluid distribution within the device is found to be uniform, and the positioning of the inlet and outlet has a minimal effect on performance metrics. However, the lateral stacking configuration of PCM units significantly improves heat transfer efficiency, increasing it by 15% compared to vertical stacking arrangements. Experimental tests confirm that increasing the inlet flow rate accelerates the phase transition process but has a marginal impact on overall energy utilization efficiency. These results provide valuable quantitative insights into optimizing the design of phase-change thermal storage devices, particularly in terms of enhancing heat transfer and overall energy efficiency. Full article

In the booming digital economy, data circulation—particularly for massive multimodal data generated by IoT sensor networks—faces critical challenges: ambiguous ownership and broken cross-domain traceability. Traditional property rights theory, ill-suited to data’s non-rivalrous nature, leads to ownership fuzziness after multi-source fusion and traceability gaps in cross-organizational flows, hindering marketization. This study aims to establish native ownership confirmation capabilities in trusted IoT-driven data ecosystems. The approach involves a dual-factor system: the collaborative extraction of text (from sensor-generated inspection reports), numerical (from industrial sensor measurements), visual (from 3D scanning sensors), and spatio-temporal features (from GPS and IoT device logs) generates unique SHA-256 fingerprints (first factor), while RSA/ECDSA private key signatures (linked to sensor node identities) bind ownership (second factor). An intermediate state integrates these with metadata, supported by blockchain (consortium chain + IPFS) and cross-domain protocols optimized for IoT environments to ensure full-link traceability. This scheme, tailored to the characteristics of IoT sensor networks, breaks traditional ownership confirmation bottlenecks in multi-source fusion, demonstrating strong performance in ownership recognition, anti-tampering robustness, cross-domain traceability and encryption performance. It offers technical and theoretical support for standardized data components and the marketization of data elements within IoT ecosystems. Full article

During the preparation of gallium nitride (GaN) single crystals by Hydride Vapor Phase Epitaxy (HVPE), variations in growth pressure within the reaction chamber can easily lead to a mismatch between vapor transport dynamics and surface reaction processes, thereby affecting crystal growth rate and uniformity. To address this issue, this study established a multi-physics coupled simulation model based on the HVPE equipment structure. By integrating reaction gas flow, heat transfer, chemical reactions, and mass transport mechanisms, systematic finite element analysis was employed to simulate the flow field distribution, thermal field stability, and precursor concentration field evolution within the reaction chamber under different growth pressures (91–141 kPa). The simulation results indicate that, on one hand, the growth rate exhibits a nearly linear increase trend with rising pressure. At lower pressures (<100 kPa), vapor transport is limited, leading to a significant decrease in growth rate, while at higher pressures (>110 kPa), growth uniformity deteriorates. Optimizing the pressure parameter can enhance both the growth rate and thickness uniformity of GaN single crystals, providing a basis for process control in the preparation of high-performance GaN devices. Full article

This study aimed to develop a high-performance Modified Activated Carbon Fiber (ACF) filter for the effective removal of Volatile Organic Compounds (VOCs) generated in workplaces and for application in indoor VOCmitigation devices. ACF was modified with CuMnOx catalysts and evaluated for the removal of formaldehyde, acetaldehyde, and benzene. The modified ACF filter was prepared by introducing CuMnOx via an impregnation method using Cu(NO₃)₂⋅3H₂O and Mn(NO₃)₂⋅6H₂O precursors, followed by a crucial high-concentration oxygen plasma surface treatment (50 sccm gas flow) to effectively incorporate oxygen functional groups, thereby enhancing catalyst dispersion and activity. Characterization of the fabricated ACF/CuMnOx composite revealed that the optimized sample, now designated ACF-P-0.1 (representing both CuMnOx catalyst impregnation and O₂ plasma treatment), exhibited uniformly dispersed CuMnOx particles (<500 nm) on the ACF surface. This stability retained a high specific surface area (1342.7 m²/g) and micropore ratio (92.23%). H₂-TPR analysis demonstrated low-temperature reduction peaks at 140 °C and 205.8 °C, indicating excellent redox properties that enable high catalytic VOC oxidation near room temperature. The oxygen plasma treatment was found to increase the interfacial reactivity between the catalyst and ACF, contributing to further enhancement of activity. Performance tests confirmed that the ACF-P-0.1 sample provided superior adsorption–oxidation synergy. Benzene removal achieved a peak efficiency of 97.5%, demonstrating optimal interaction with the microporous ACF structure. For formaldehyde, a removal efficiency of 96.6% was achieved within 30 min, significantly faster than that of Raw ACF, highlighting the material’s ability to adsorb VOCs and subsequently oxidize them with high efficiency. These findings suggest that the developed ACF/CuMnOx composite filters can serve as promising materials for VOCs removal in indoor environments such as printing, coating, and conductive film manufacturing processes. Full article