Search Results (2,741)

Microphysiological systems (MPSs) have emerged as alternatives to animal testing in drug development, following the FDA Modernization Act 2.0. Double-layer channel-type MPS chips with porous membranes are widely used for modeling various organs, including the intestines, blood–brain barrier, renal tubules, and lungs. However, these chips faced challenges owing to optical interference caused by light scattering from the porous membrane, which hinders cell observation. Trans-epithelial electrical resistance (TEER) measurement offers a non-invasive method for assessing barrier integrity in these chips. However, existing electrode-integrated MPS chips for TEER measurement have non-uniform current densities, leading to compromised measurement accuracy. Additionally, chips made from polydimethylsiloxane have been associated with drug absorption issues. This study developed an electrode-integrated MPS chip for TEER measurement with a uniform current distribution and minimal drug absorption. Through a finite element method simulation, electrode patterns were optimized and incorporated into a polyethylene terephthalate (PET)-based chip. The device was fabricated by laminating PET films, porous membranes, and patterned gold electrodes. The chip’s performance was evaluated using a perfused Caco-2 intestinal model. TEER levels increased and peaked on day 5 when cells formed a monolayer, and then they decreased with the development of villi-like structures. Concurrently, capacitance increased, indicating microvilli formation. Exposure to staurosporine resulted in a dose-dependent reduction in TEER, which was validated by immunostaining, indicating a disruption of the tight junction. This study presents a TEER measurement MPS platform with a uniform current density and reduced drug absorption, thereby enhancing TEER measurement reliability. This system effectively monitors barrier integrity and drug responses, demonstrating its potential for non-animal drug-testing applications. Full article

Deep learning algorithms have significantly reduced the computational time and improved the spatial resolution of particle image velocimetry (PIV). However, the models trained on synthetic datasets might have degraded performances on practical particle images due to domain gaps. As a result, special residual patterns are often observed for the vector fields of deep learning-based estimators. To reduce the special noise step by step, we employ a denoising diffusion model (FlowDiffuser) for PIV analysis. And a data-hungry iterative denoising diffusion model is trained via a transfer learning strategy, resulting in our PIV-FlowDiffuser method. Specifically, we carry out the following: (1) pre-training a FlowDiffuser model with multiple optical flow datasets of the computer vision community, such as Sintel and KITTI; (2) fine-tuning the pre-trained model on synthetic PIV datasets. Note that the PIV images are upsampled by a factor of two to resolve small-scale turbulent flow structures. The visualized results indicate that our PIV-FlowDiffuser effectively suppresses the noise patterns. Therefore, the denoising diffusion model reduces the average endpoint error ($AEE$) by $59.4\%$ over the RAFT256-PIV baseline on the classic Cai’s dataset. In addition, PIV-FlowDiffuser exhibits enhanced generalization performance on unseen particle images due to transfer learning. Overall, this study highlights transfer-learning-based denoising diffusion models for PIV. Full article

This article presents a principled framework for selecting and tuning classical computer vision algorithms in the context of optical displacement sensing. By isolating key factors that affect algorithm behavior—such as feed window size and motion step size—the study seeks to move beyond intuition-based practices and provide rigorous, repeatable performance evaluations. Computer vision-based optical odometry sensors offer non-contact, high-precision measurement capabilities essential for modern metrology and robotics applications. This paper presents a systematic comparative analysis of three classical tracking algorithms—phase correlation, template matching, and optical flow—for 2D surface displacement measurement using synthetic image sequences with subpixel-accurate ground truth. A virtual camera system generates controlled test conditions using a multi-circle trajectory pattern, enabling systematic evaluation of tracking performance using 400 × 400 and 200 × 200 pixel feed windows. The systematic characterization enables informed algorithm selection based on specific application requirements rather than empirical trial-and-error approaches. Full article

Atmospheric turbulence severely limits the stability and reliability of free-space optical (FSO) uplinks by inducing wavefront distortions and random intensity fluctuations. This study investigates the use of reinforcement learning (RL) with beacon-based feedback for adaptive beam shaping in a spatial light modulator (SLM)-controlled FSO link. The RL agent dynamically adjusts phase patterns to maximize received signal strength, while the beacon channel provides turbulence estimates that guide the optimization process. Experiments under low, moderate, and high turbulence levels demonstrate that incorporating beacon feedback can enhance link stability in severe conditions, reducing signal variability and suppressing extreme fluctuations. In low-turbulence scenarios, the performance is comparable to non-feedback operation, whereas under high turbulence, beacon-assisted control consistently achieves lower coefficients of variation and improved bit error rate (BER) performance. Under high turbulence replay experiments—where the best-performing RL-learned phase patterns are reapplied without learning—further show that configurations trained with feedback retain robustness, even without real-time turbulence measurements under high turbulence. These results highlight the potential of integrating contextual feedback with RL to achieve turbulence-resilient and stable optical uplinks in dynamic atmospheric environments. Full article

Background/Objectives: This study aimed to examine the effects of an eight-week wearable resistance (WR) training program on jump performance and jump kinematics in experienced senior female volleyball players. It was hypothesised that using WR would increase training load, thereby enhancing vertical jump performance and influencing kinematic movement patterns. Methods: Sixteen competitive female volleyball players (mean age: 23.5 ± 3.24 years; mean weight: 66.8 ± 6.9 kg; mean height: 174.7 ± 5.8 cm) participated in the study. Participants were randomly assigned to either a control group (n = 8) or an intervention group (n = 8) that trained with calf-mounted WR. The intervention group performed supervised resistance training sessions twice per week for eight weeks, totalling 16 sessions. Jump performance was assessed using an Infrared Optical Contact Grid (MuscleLab, Ergotest Innovation AS, Norway), and jump kinematics were measured with the Xsens Link motion capture system (Movella, The Netherlands). Results: The WR group demonstrated a statistically significant improvement in vertical jump height (p = 0.031), with no significant changes in kinematic variables. The control group, however, showed a significant increase in T8–pelvis flexion during the countermovement jump (CMJ) following the intervention period. Conclusions: Eight weeks of WR training can improve CMJ performance in-season among experienced female volleyball players without affecting movement kinematics. Future research should investigate optimal loading strategies and long-term adaptations. These findings suggest that integrating small wearable loads into regular volleyball practice can help athletes maintain and improve explosive performance without disrupting normal training routines. Full article

Therapeutic plasma exchange (TPE) is increasingly recognized as a critical escalation therapy for managing acute multiple sclerosis (MS) relapses refractory to high-dose corticosteroids. Neuropathological and clinical evidence implicate humoral immune mechanisms, particularly autoantibodies, immune complexes, and complement activation, as key pathogenic drivers in a subset of MS attacks, notably those consistent with immunopathological pattern II. By removing these circulating immune effectors, TPE provides a rational strategy to dampen inflammation and promote neurological recovery. This review integrates current mechanistic insights with clinical efficacy data and practical implementation strategies for TPE in corticosteroid-refractory MS. Evidence from randomized controlled trials and observational cohorts demonstrates moderate-to-marked functional improvement in 40–60% of patients, with the greatest benefit observed when therapy is initiated within 14 days of symptom onset and cases demonstrating active inflammatory lesions on MRI. Predictors of a favorable response include younger age, short disease duration, severe syndromes involving optic nerve, brainstem, or spinal cord, and CSF markers of intrathecal B-cell activity. Although TPE is generally well tolerated in experienced centers, its broader adoption of TPE is limited by variability in access, institutional protocols, and provider familiarity. Standardization of treatment algorithms, validation of predictive biomarkers, and incorporation into streamlined clinical pathways are critical to maximizing its clinical impact. Future priorities include comparative trials against alternative escalation therapies, biomarker-guided patients’ selection, and comprehensive health-economic evaluations. Taken together, current evidence and recommendations from major neurology and apheresis societies support TPE as a valuable therapeutic modality capable of significantly improving relapse outcomes in appropriately selected MS patients. Full article

In recent years, apatite-based materials have garnered significant interest, particularly for applications in tissue engineering. Apatite is most commonly employed as a coating for metallic implants, as a component in composite materials, and as scaffolds for bone and dental tissue regeneration. Among its various forms, hydroxyapatite (HAP) is the most widely used, owing to its natural occurrence in human and animal hard tissues. An emerging area of research involves the use of fluoride-substituted apatite, particularly fluorapatite (FAP), which can serve as a direct fluoride source at the implant site, potentially offering several biological and therapeutic advantages. However, substituting HAP with FAP may lead to unforeseen changes in material behavior due to the differing physicochemical properties of these two calcium phosphate phases. This study investigates the effects of replacing hydroxyapatite with fluorapatite in ceramic–polymer composite materials incorporating β-1,3-glucan as a bioactive polymeric binder. The β-1,3-glucan polysaccharide was selected for its proven biocompatibility, biodegradability, and ability to form stable hydrogels that promote cellular interactions. Nitrogen adsorption analysis revealed that FAP/glucan composites had a significantly lower specific surface area (0.5 m²/g) and total pore volume (0.002 cm³/g) compared to HAP/glucan composites (14.15 m²/g and 0.03 cm³/g, respectively), indicating enhanced ceramic–polymer interactions in fluoride-containing systems. Optical profilometry measurements showed statistically significant differences in profile parameters (e.g., Rp: 134 μm for HAP/glucan vs. 352 μm for FAP/glucan), although average roughness (Ra) remained similar (34.1 vs. 27.6 μm, respectively). Microscopic evaluation showed that FAP/glucan composites had smaller particle sizes (1 μm) than their HAP counterparts (2 μm), despite larger primary crystal sizes in FAP, as confirmed by TEM. XRD analysis indicated structural differences between the apatites, with FAP exhibiting a reduced unit cell volume (524.6 ų) compared to HAP (528.2 ų), due to substitution of hydroxyl groups with fluoride ions. Spectroscopic analyses (FTIR, Raman, ³¹P NMR) confirmed chemical shifts associated with fluorine incorporation and revealed distinct ceramic–polymer interfacial behaviors, including an upfield shift of PO₄³⁻ bands (964 cm⁻¹ in FAP vs. 961 cm⁻¹ in HAP) and OH vibration shifts (3537 cm⁻¹ in FAP vs. 3573 cm⁻¹ in HAP). The glucan polymer showed different hydrogen bonding patterns when combined with FAP versus HAP, as evidenced by shifts in polymer-specific bands at 888 cm⁻¹ and 1157 cm⁻¹, demonstrating that fluoride substitution significantly influences ceramic–polymer interactions in these bioactive composite systems. Full article

This study investigates the magneto-optical properties of a ferrofluid using an accessible Ferrocell device. Our findings demonstrate that the ferrofluid’s behavior is critically dependent on its concentration. At high concentrations, the medium is optically dense, with inter-particle scattering and absorption dominating, which prevents the formation of clear light patterns. However, with intermediate dilution, the system enters a “pattern formation zone” where the magnetic field effectively aligns the nanoparticles, creating complex, visible light patterns like horocycles. The appearance of these patterns provides evidence of field-induced ordering and structural coloration. The colors observed are not due to pigments, but result from the interaction of light with the periodic structures formed by the aligned nanoparticles. Our analysis, supported by the Mueller matrix framework, confirms that the ferrofluid acts as a retarder. The birefringence induced by the magnetic field varies across the film, leading to a chromatic dispersion that selectively suppresses certain wavelengths. This process explains how a specific color, such as blue, can be blocked at one location while others pass through, creating structural colors observed in the patterns. Full article

Cloud cover can delay landslide detection in optical satellite imagery for weeks, complicating disaster response. Synthetic Aperture Radar (SAR) backscatter imagery, which is widely used for monitoring floods and avalanches, remains underutilised for landslide detection due to a limited understanding of landslide signatures in SAR data. We developed a conceptual model of landslide expression in SAR backscatter (σ°) change images through iterative investigation of over 1000 landslides across 30 diverse study areas. Using multi-temporal composites and dense time series Sentinel-1 C-band SAR data, we identified characteristic patterns linked to land cover, terrain, and landslide material. The results showed either increased or decreased backscatter depending on environmental conditions, with reduced visibility in urban or mixed vegetation areas. Detection was also hindered by geometric distortions and snow cover. The diversity of landslide expression illustrates the need to consider local variability and multi-track (ascending and descending) satellite data in designing representative training datasets for automated detection models. The conceptual model was applied to three recent disaster events using the first post-event Sentinel-1 image, successfully identifying previously unknown landslides before optical imagery became available in two cases. This study provides a theoretical foundation for interpreting landslides in SAR imagery and demonstrates its utility for rapid landslide detection. The findings support further exploration of rapid landslides in SAR backscatter data and future development of automated detection models, offering a valuable tool for disaster response. Full article

Aerosols from natural and anthropogenic sources exert significant yet highly variable influences on the Earth’s radiative balance characterized by pronounced spatial and temporal heterogeneity. Accurate quantification of these effects is crucial for enhancing climate projections and informing effective mitigation strategies. In this study, we evaluated the performance of three widely used aerosol optical depth (AOD) datasets—MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2), MODIS Aqua, and MODIS Terra—by comparing them against ground-based AERONET observations from ten stations located within the dust belt region. Statistical assessments included coefficient of determination (R²), correlation coefficient (R), Index of Agreement (IOA), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Relative Mean Bias (RMB), and standard deviation (SD). The results indicate that MERRA-2 showed the highest agreement (R = 0.76), followed by MODIS Aqua (R = 0.75) and MODIS Terra (R = 0.73). Seasonal and annual AOD climatology maps revealed comparable spatial patterns across datasets, although MODIS Terra consistently reported slightly higher AOD values. These findings provide a robust assessment and reanalysis of satellite AOD products over arid regions, offering critical guidance for aerosol modeling, data assimilation, and climate impact studies. Full article

Electromagnetic micro-electro-mechanical system (MEMS) micromirrors are widely used in optical scanning systems but often encounter mechanical crosstalk due to the use of shared drive coils. This phenomenon leads to parasitic motion along the slow axis during fast-axis operation, resulting in undesirable elliptical scanning patterns that degrade image quality. To tackle this issue, a hybrid actuation scheme is proposed in which a piezoelectric actuator drives the fast axis through an S-shaped spring structure, achieving a resonance frequency of 792 Hz, while the slow axis is independently driven by an electromagnetic actuator operating in quasi-static mode. Finite element simulations and experimental measurements validate that the proposed decoupled design significantly suppresses mechanical crosstalk. When the fast axis is driven to a 40° optical scan angle, the hybrid system reduces the parasitic slow-axis deflection (typically around 1.43°) to a negligible level, thereby producing a clean single-line scan. The piezoelectric fast axis exhibits a quality factor of Q = 110, while the electromagnetic slow axis achieves a linear 20° deflection at 20 Hz. This hybrid design facilitates a distortion-free field of view measuring 40° × 20° with uniform line spacing, presenting a straightforward and effective solution for high-precision scanning applications such as LiDAR (Light Detection and Ranging) and structured light projection. Full article

Micro- and nanopatterning is crucial for advanced photonic, electronic, and sensing devices. Yet achieving large-area periodic nanostructures with a 75 nm half-pitch on low-cost laboratory systems remains difficult, because conventional near-ultraviolet laser interference lithography (LIL) suffers from Gaussian-beam non-uniformity and a narrow exposure latitude. Here, we report a cost-effective deep-ultraviolet (DUV) dual-beam LIL system based on a 266 nm laser and diffractive flat-top beam shaping, enabling large-area patterning of periodical nanostructures. At this wavelength, a moderate half-angle can be chosen to preserve a large beam-overlap region while still delivering 150 nm period (75 nm half-pitch) structures. By independently tuning the incident angle and beam uniformity, we pattern one-dimensional (1D) gratings and two-dimensional (2D) arrays over a Ø 1.0 cm field with critical-dimension variation < 5 nm (1σ), smooth edges, and near-vertical sidewalls. As a proof of concept, we transfer a 2D pattern into Si to create non-metal-coated nanodot arrays that serve as surface-enhanced Raman spectroscopy (SERS) substrates. The arrays deliver an average enhancement factor of ~1.12 × 10⁴ with 11% intensity relative standard deviation (RSD) over 65 sampling points, a performance near the upper limit of all-dielectric SERS substrates. The proposed method overcomes the uneven hotspot distribution and complex fabrication procedures in conventional SERS substrates, enabling reliable and large-area chemical sensing. Compared to electron-beam lithography, the flat-top DUV-LIL approach offers orders-of-magnitude higher throughput at a fraction of the cost, while its centimeter-scale uniformity can be scaled to full wafers with larger beam-shaping optics. These attributes position the method as a versatile and economical route to large-area photonic metasurfaces and sensing devices. Full article

Achieving rapid and accurate object classification holds significant importance in various domains. However, conventional vision-based techniques suffer from several limitations, including high data redundancy and strong dependence on image quality. In this work, we present a high-speed, image-free object classification method based on dual-pixel measurement and normalized central moment invariants. Leveraging the complementary modulation capability of a digital micromirror device (DMD), the proposed system requires only five tailored binary illumination patterns to simultaneously extract geometric features and perform classification. The system can achieve a classification update rate of up to 4.44 kHz, offering significant improvements in both efficiency and accuracy compared to traditional image-based approaches. Numerical simulations verify the robustness of the method under similarity transformations—including translation, scaling, and rotation—while experimental validations further demonstrate reliable performance across diverse object types. This approach enables real-time, low-data throughput, and reconstruction-free classification, offering new potential for optical computing and edge intelligence applications. Full article

Infrared optical vortices are used in the field of optical communications at wavelengths around 1550 nm. A versatile method to generate them is with a Spatial Light Modulator (SLM); however, they are expensive devices and cannot be easily integrated into compact systems, as opposed to Holographic Optical Elements (HOEs), which are lightweight, smaller and thinner, and easier to align and combine with other optical systems. In this work, volume transmission HOEs have been recorded in a commercial photopolymer, Bayfol HX200, by exposing it to the interference pattern obtained with an optical vortex (obtained with an SLM) and a plane wave in the visible range. When illuminated with a plane wave at 1534 nm, the diffracted beam carried an optical vortex. An experimental efficiency of approximately 45% at that wavelength has been obtained, proving the viability of the method. Full article

We report Al/ZnO/Al optoelectronic synaptic devices fabricated on c-plane and m-plane sapphire substrates using a sol–gel process. The devices exhibit essential synaptic behaviors such as excitatory postsynaptic current modulation, paired-pulse facilitation, and long-term learning–forgetting dynamics described by Wickelgren’s power law. Comparative analysis reveals that substrate orientation strongly influences memory performance: devices on m-plane consistently show higher EPSCs, slower decay rates, and superior retention compared to c-plane counterparts. These characteristics are attributed to crystallographic effects that enhance carrier trapping and persistent photoconductivity. To demonstrate their practical applicability, 3 × 3-pixel arrays of adjacent devices were constructed, where a “T”-shaped optical pattern was successfully encoded, learned, and retained across repeated stimulation cycles. These results highlight the critical role of substrate orientation in tailoring synaptic plasticity and memory retention, offering promising prospects for ZnO-based optoelectronic synaptic arrays in in-sensor neuromorphic computing and artificial visual memory systems. Full article