Search Results (6,568)

Aesthetic medicine has progressed from the early 2000s fascination with bio-stimulation to the current dominance of hyaluronic acid (HA) fillers, prized for immediate, predictable, and reversible volumizing effects. Recently, demand for more natural results, stronger emphasis on skin quality, and increased post-pandemic self-scrutiny have renewed interest in regenerative strategies, sometimes called the “second wave of bio-stimulation.” This trend highlights the need for clearer terminology and a cautious, evidence-based reading of proposed biological mechanisms. This narrative review proposes a framework in which bio-regeneration denotes a hypothesized, controlled induction of physiological processes, fibroblast activation, collagen and elastin synthesis, extracellular matrix remodeling, and immune modulation, potentially producing sustained improvements in dermal structure and function beyond simple filling. Among emerging technologies, polyethylene glycol diglycidyl ether (PEGDE) cross-linking is reported to create a stable, flexible HA scaffold with homogeneous tissue integration, favorable rheology, thermal stability, and a reduced inflammatory profile, supporting safer multimodal use with energy-based devices. The framework is illustrated with PEGDE-crosslinked HA combined with low-concentration calcium hydroxyapatite (CaHA), exemplified by a PEGDE-HA filler containing CaHA microspheres plus glycine and L-proline. These formulations aim to deliver immediate correction via HA and delayed stimulatory effects possibly driven by gradual CaHA exposure and macrophage-associated signaling. Available clinical, imaging, and histological observations, including prospective ultrasound and biopsy assessments, suggest progressive dermal thickening and predominant type I collagen expression, without pathological inflammation or granuloma formation. Although evidence remains preliminary and largely non-comparative, findings are compatible with controlled remodeling and resolving inflammation; however, the underlying mechanism and any ‘regenerative’ versus ‘reparative’ classification require controlled comparative studies. Full article

Healthcare visualization has become a crucial approach for interpreting complex medical data, supporting informed clinical decision-making, and enhancing public health management. However, existing reviews tend to focus on specific technologies or application scenarios, offering limited insight into the field’s overall knowledge structure, developmental trajectory, and interdisciplinary integration. To address this gap, this study systematically reviews 1121 publications from 1994 to 2025 indexed in the Web of Science Core Collection. By combining bibliometric analysis with qualitative assessment, it maps the field’s evolution and underlying research paradigms. The findings reveal a clear shift from early innovation in technical tools toward the realization of clinical value, giving rise to an integrated research system that connects technology, data, clinical practice, and public health. Recent research has progressed beyond initial explorations of medical imaging, standalone devices, and isolated techniques, moving instead toward core domains such as immersive medical visualization, medical data visualization and analytics, health information systems and decision support, AI-assisted epidemic prediction and diagnosis, and integrated IoT-based healthcare frameworks. Looking ahead, an assessment of future trends suggests that, among other directions, the deep integration of explainable artificial intelligence (XAI) with visualization analysis, the development of IoT-driven real-time interactive systems, and the extension of visualization-enabled services from clinical applications toward inclusive population-level health coverage represent core driving forces for the future development of this field. These insights offer strategic guidance for future research, inform the design principles of next-generation visualization systems, and provide new models of interdisciplinary collaboration. The results also offer evidence-based support for health resource planning, technological innovation, and policy formulation. Full article

This study investigates efficient thermal-image human pose estimation under edge deployment constraints for smart home activity recognition. A single-person thermal dataset of 2500 images was collected and annotated with 17 body keypoints. YOLO11-pose and YOLOv8-pose models were trained and evaluated across all five model scales (n–x) at three input resolutions 640 × 512, 320 × 256, and 160 × 128 px. The accuracy was evaluated using box mean Average Precision (mAP50–95), pose mAP50–95, and Object Keypoint Similarity (OKS) metrics. Runtime performance was assessed using per-image latency and power measurements on three NVIDIA Jetson platforms: Orin Nano 4 GB, Orin Nano 8 GB and AGX Orin 64 GB, using PyTorch and TensorRT at FP32, FP16, INT8 precision. Human detection remained consistently high across model variants, whereas pose accuracy decreased as the input resolution was reduced. TensorRT FP16 preserved pose accuracy relative to PyTorch and TensorRT FP32, with minimal changes in OKS and pose mAP50–95, while notably reducing per-image latency and power consumption. INT8 further reduced power consumption and in some configurations improved latency, but caused configuration-dependent losses in OKS and pose mAP50–95. The findings indicate that FP16 offers the best accuracy–efficiency balance for thermal pose estimation on edge devices, while practical feasibility depends on device capabilities and memory limitations. Full article

Organ-on-a-chip (OoC) technologies, also termed microphysiological systems (MPSs), integrate microfluidics, engineered biomaterials, human-derived cells, and on-chip biosensing to model human physiology in microscale devices that deliver quantitative, time-resolved readouts. This review surveys the 2010–2025 literature, emphasizing how sensing, standardized sampling, and analytics enable clinical concordance and fit-for-purpose regulatory use. We synthesize advances in (i) materials, fabrication, and microfluidic design; (ii) organ- and disease-focused case studies; and (iii) translational benchmarks that align chip outputs with clinical pharmacokinetics, toxicology, and biomarker datasets. Across organ systems, platforms increasingly incorporate vascularization, immune components, and organoid hybrids, paired with real-time measurements of barrier integrity, metabolism, electrophysiology, and secreted biomarkers using impedance (TEER), electrochemical, and optical modalities. Representative benchmarking studies report cardiac OoCs achieving AUROC ≥ 0.85 for torsadogenic risk classification, and renal chips improving prediction of transporter-mediated clearance relative to conventional in vitro assays. We summarize validation approaches and regulatory developments relevant to new approach methodologies, including the FDA Modernization Act 2.0, and discuss how AI and multi-omics can automate signal and image analysis, harmonize cross-platform datasets, and support digital-twin workflows that couple OoC measurements to in silico models. Overall, biosensor-enabled OoCs are progressing toward quantitatively benchmarked platforms for safety pharmacology, ADME/PK–PD, and precision medicine. Full article

The echelle spectrometer utilizes an echelle grating as the primary dispersive element, combined with a prism or planar grating for cross-dispersion, to form a two-dimensional spectral image on an area-array Charge-Coupled Device (CCD). Compared with traditional spectrometers, this configuration provides superior spectral resolution, broader wavelength coverage, enhanced transient direct-reading capability, and higher energy throughput within a similar footprint. However, the use of area-array detectors significantly increases system cost, limiting adoption in cost-sensitive applications. To reduce cost while maintaining performance, we introduce a digital micromirror device (DMD) as a spatial light modulator to replace the traditional area-array detector, paired with a highly sensitive photomultiplier tube (PMT) for signal acquisition. The designed system operates across a wavelength range of 270 to 800 nm within a compact footprint of approximately 307 mm × 210 mm × 150 mm. The focused spot is accurately positioned on the DMD surface across the entire band, with the root mean square (RMS) spot radius smaller than a single micromirror’s size. Spectral information is efficiently coupled into the PMT via a focusing mirror by selectively flipping the DMD micromirrors for detection. Full article

This paper presents a lightweight hyper-network architecture for cross-scale multi-task object classification, addressing the critical challenge of gradient interference in joint learning scenarios. We propose a HyperConv module integrated into a slim ResNet-12 backbone, which dynamically generates task-adaptive 3 × 3 convolutional kernels from compact two-dimensional latent vectors. This design allows explicit control over gradient flows for different tasks with minimal parameter overhead (only 3.2% additional parameters). Our framework incorporates adversarial regularization via a Gradient Reversal Layer (GRL) and dynamic task-weight scheduling to mitigate gradient conflicts across domains. Experiments on both natural image datasets (Mini-ImageNet and CIFAR-100) and remote sensing benchmarks (EuroSat and UCMerced_LandUse) demonstrate statistically significant improvements over conventional shared-parameter baselines. The proposed method effectively reduces negative transfer, enhances feature representation, and offers a practical solution for on-device multi-task learning in resource-constrained remote sensing applications such as UAVs and edge satellites. Full article

Accurate urban noise mapping requires granular traffic flow characterization aligned with specific acoustic models, such as CNOSSOS-EU. Existing monitoring solutions often lack the specific categorization capabilities, cost-effectiveness, or flexibility required for large-scale deployment in resource-constrained environments. To address this challenge, the present study describes the development of a real-time multi-vehicle recognition system based on low-cost edge computing hardware, specifically a Raspberry Pi 4 coupled with a Coral TPU accelerator. The proposed methodology integrates a quantized YOLOv8 convolutional neural network (CNN) with a tracking algorithm to enable real-time detection and classification of vehicles into five distinct classes, allowing for precise aggregation according to CNOSSOS-EU standards. The model was trained on a proprietary dataset of 15,000 images and subjected to 8-bit post-training quantization to optimize inference speed. Experimental results demonstrate that the system achieves an inference speed of 14 FPS and a mean Average Precision (mAP@50) of 92.2% in daytime conditions, maintaining robust performance on embedded devices. In a real-world case study, the proposed system significantly outperformed a commercial traffic monitoring solution, achieving a weighted percentage error of just 6.6% compared to the commercial system’s 59.9%, effectively bridging the gap between manual counting accuracy (1.4% error) and automated efficiency. Full article

Dynamic wavefront control plays a crucial role in advancing terahertz (THz) high-precision non-destructive testing, wireless communication and high-resolution imaging. However, existing approaches to THz dynamic wavefront control suffer from inherent limitations, such complex structures, narrow operational bandwidth, and the ability to tune only a single function, significantly restricting their practical applications. To overcome these challenges, we propose a dynamic reflective THz metasurface based on nested split-ring unit cells. The nested unit cell consists of an outer double-split VO₂ ring resonator and an inner single-split aluminum ring deposited on a central VO₂ circular patch. By, respectively, rotating the inner and outer rings in the insulator and metal states of VO₂, independent full 2π phase coverage at 1.07 THz can be achieved in both VO₂ states while maintaining high polarization-conversion efficiency with a PCR exceeding 0.98, thereby enabling efficient dynamic wavefront control. Using these unit cells, we constructed three distinct reflective metasurfaces that, respectively, generate broadband focusing beams with tunable focal lengths, broadband vortex beams with different topological charges, and a broadband beam that can be switched between focusing and vortex modes by changing the state of VO₂. The design offers considerable flexibility for developing compact, multifunctional THz devices, with promising potential for integrated THz systems, high-capacity communications, and high-resolution imaging. Full article

Cancer is one of the global health problems that affects millions of people every year. Biopsies are among the standard methods for detecting and confirming a cancer diagnosis. Performing this study manually poses several challenges due to tissue movement and the difficulty of precisely locating the target, as is often the case in lung biopsies. This study presents the design and implementation of an autonomous image processing algorithm included in a closed-loop controller that drives the activity of a multi-degree-of-freedom (six) robotic manipulator that performs emulated tissue biopsies. A realistic lung motion emulator, based on a two-degree-of-freedom robotic device with a photon emitter (to simulate radiopharmaceutical identification of cancerous tissue), was used to test the proposed automatic biopsy collector. Applying image processing to detect cancer tissue enables the identification of the centroid and tumor boundaries. Using the detected centroid coordinates, the reference trajectory of the end effector (biopsy needle) was automatically determined. A finite-time convergent controller was implemented to guide the robotic manipulator’s motion towards the tumor position within a specified time window. The controller was evaluated using a digital twin representation of the entire robotic system and using an experimental device working on the simulated mobile tumor emulator. Evaluation of simulated tumor detection and reference trajectory tracking effectiveness was used to validate the operation of the proposed automatic robotic lung biopsy sampler. The application of the controller allows one to track the position of the emulated tumor with a deviation of 0.52 mm and a settling time of less than 1 s. Full article

Two-dimensional (2D) materials are competitive in a diverse range of areas, spanning from electronic and optoelectronic devices to wearable devices, due to their unique physical and chemical characteristics, as well as remarkable flexibility. As a typical 2D material, lead iodide (PbI₂), featuring a high atomic number and tunable band gap, has been extensively studied in many applications of electroluminescent (EL) devices, photodetectors, and perovskite solar cells. However, high-performance PbI₂-based photodetectors remain a challenge. Herein, we present a high-performance flexible photodetector based on 2D layered PbI₂ nanoplates, which were synthesized via a straightforward air sublimation method. The PbI₂-based photodetector exhibits an excellent photoresponse and the highest responsivity peaks at 34 A/W at 405 nm, together with an ultrahigh transient switching on/off current ratio of 10⁷. Due to a low dark current (10⁻¹⁴ A), the device exhibits an extremely low noise level (<10⁻²⁶ A²Hz⁻¹) and acceptable detectivity (2 × 10¹⁰ Jones). Furthermore, remarkable mechanical flexibility was observed in the device on a PET substrate, preserving both its electrical conductance and photoresponse stability after 560 bending cycles. Finally, high-resolution imaging applications were implemented under a 100 Hz modulated light signal. This work highlights the superior optoelectrical properties of 2D PbI₂ growth by the in-air sublimation method and proves its promising future in flexible and wearable optoelectronic devices. Full article

Deep learning-based sheep face recognition technology significantly enhances the automation of individual sheep identification, providing critical technical support for smart livestock farming and precision agriculture. However, in real farming environments, factors such as complex backgrounds, illumination variations, and the high visual similarity of sheep faces severely constrain the comprehensive performance of recognition systems regarding accuracy and real-time capability. To address these challenges, we propose a cascaded framework comprising the WRT-DETR model for detection and LG-MobileViT for identification. WRT-DETR integrates multi-scale wavelet residual modeling and adaptive feature interaction into the RT-DETR architecture to effectively handle complex backgrounds. Subsequently, LG-MobileViT utilizes local–global collaborative modeling to distinguish fine-grained features while maintaining a lightweight footprint suitable for edge devices. Experiments conducted on a dataset of 400 individuals and 20,000 images demonstrate that WRT-DETR achieves 92.5% mAP50 in detection tasks. Furthermore, LG-MobileViT attains 98.97% recognition accuracy with a parameter size of only 4.57 MB. On edge computing platforms, the integrated system reaches an inference speed approaching 100 FPS. These results confirm that the proposed framework offers an efficient, reliable technical solution for non-contact, precise sheep identification in practical precision agriculture scenarios. Full article

Background: Swept-source optical coherence tomography angiography (SS-OCTA) enables rapid assessment of retinal microvasculature. However, cross-platform comparability remains limited by device-specific acquisition and image quality characteristics. This study prospectively compared two novel SS-OCTA systems, DREAM (200 kHz) and BMizar (400 kHz). Methods: Fifty eyes from 25 healthy participants underwent 3 mm × 3 mm macular OCTA imaging with both devices in a single session. Images were analysed using OCTAVA to extract foveal avascular zone (FAZ) area, vessel area density (VAD), total vessel length (TVL), node counts, fractal dimension (FD), median vessel length (MVL) in SCP, and mean vessel diameter (MVD) in DCP. Image quality was assessed using FAZ-noise rate, contrast-to-noise ratio (CNR), and FAZ noise-floor standard deviation. Paired comparisons were performed using Wilcoxon signed-rank tests and Cliff’s delta. Results: BMizar acquisition time was shorter than DREAM for the evaluated 3 × 3 mm protocol (median 5.36 s vs. 9.93 s), reflecting differences in A-scan rate and protocol implementation; acquisition time is therefore reported descriptively. In the SCP, DREAM yielded lower VAD (41.9% vs. 48.8%) and fewer nodes (1547 vs. 1879) but exhibited markedly less background noise (noise-floor SD 4.1 vs. 57.9) and substantially higher CNR (16.7 vs. 0.82). DREAM also showed longer MVL (45 vs. 39 µm) and higher FD (1.98 vs. 1.97; δ = 0.90). In the DCP, DREAM demonstrated smaller FAZ areas (0.27 vs. 0.42 mm²), thinner MVD (14 vs. 25 µm), higher node counts (3144 vs. 2301), longer TVL (223.6 vs. 206.2 mm), and higher FD (1.98 vs. 1.97), whereas VAD was higher on BMizar (32.96% for DREAM vs. 49.93% for BMizar). FAZ-noise rates were consistently higher for BMizar in both plexuses. Conclusions: Both devices provide reliable SS-OCTA imaging, but with distinct strengths. DREAM delivers higher vascular continuity and more reliable FAZ and DCP quantification, whereas BMizar achieves faster acquisition at the cost of noise, inflating SCP density and distorting FAZ-based metrics. Awareness of these characteristics is essential to ensure the valid use of OCTA biomarkers in clinical and research applications. Full article

Vision transformers (ViTs) have emerged as one of the most popular computer vision models, achieving remarkable performance in image recognition. However, ViTs require large-scale, high-dimensional matrix computations, and traditional digital accelerators, such as graphics processing units (GPUs), have memory bandwidth limitations, leading to higher latency, increased energy consumption, and larger area. To address this challenge, this paper proposes a memristor-based analog accelerator that leverages memristor crossbar arrays for in-memory computing, reducing data movement and improving computational efficiency. Considering the non-ideal characteristics of memristor devices and the influence of analog circuitry, we incorporate Gaussian-distributed analog computation error at each step and memristor non-ideality modeling into the ViT inference to enable realistic evaluation under hardware-level conditions. Experimental evaluation on ImageNet-1k dataset with TIMM-pretrained ViT models shows that the proposed analog accelerator can achieve the same Top-1 accuracy as a custom-designed 5 nm digital baseline accelerator, even with ~35% analog computation error and ~10% memristor conductance variation injected at each step. Compared to the digital counterpart, the proposed design achieves an 11.9× reduction in energy-delay product (EDP) and a 137.2× reduction in energy-delay-area product (EDAP). Full article

Injection-related errors remain a common clinical issue and can cause patient discomfort, hematoma formation, and procedural inefficiencies. The visualization of subcutaneous veins using near-infrared (NIR) imaging has gained attention as an effective approach to reducing such errors, as blood exhibits a higher absorption of NIR light than surrounding tissue. In this study, a low-cost, non-invasive vein visualization system is presented to support safer and more accurate venous access. The proposed system integrates an NIR illumination source and a modified webcam within a compact equipment enclosure, allowing subjects to be conveniently examined by placing their arm inside the device. Vein images are automatically acquired using a laptop-based platform, followed by digital image processing techniques for vein enhancement and visualization. Laboratory-scale experiments were conducted on healthy volunteers to evaluate system performance under multiple conditions, including different vein locations (upper and lower arm regions), varying distances between the NIR light source and the arm (15 cm and 20 cm), and ambient illumination interference (light sources on and off). The experimental results demonstrate the successful implementation and reliable operation of the proposed system. Effective vein visualization was achieved across all test conditions, as confirmed by qualitative visual assessment and quantitative image quality metrics, including the Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE). Overall, the proposed system offers a practical, accessible, and cost-effective solution for vein visualization, showing strong potential for clinical and experimental applications aimed at reducing injection errors and improving venous access reliability. Full article

Electrophoretic Displays (EPDs) are widely adopted in e-readers and portable devices due to their ultra-low power consumption and eye-friendly reflective characteristics. However, inherent hardware limitations, such as low resolution, slow response speed, and display degradation, frequently result in blurred strokes and degraded text readability. While traditional driving waveform optimizations can mitigate these issues, they are device-dependent and require extensive manual calibration. To address these challenges, this paper proposes an Improved Whale Optimization Algorithm-based Multi-scale Fusion Attention-enhanced SwinIR (IWOA-MFA-SwinIR) model for super-resolution and recognition of text images on EPDs. Structurally, the model incorporates a multi-scale fused attention (MFA) module that synergistically integrates channel, spatial, and gated attention mechanisms to precisely capture high-frequency text details while suppressing background noise within the SwinIR architecture. Furthermore, to enhance model robustness and eliminate manual tuning, an Improved Whale Optimization Algorithm (IWOA) is employed to adaptively optimize critical hyperparameters, including embedding dimension (d), attention head count (h), learning rate ($lr$), and dimensionality reduction coefficient (r). Experiments conducted on the TextZoom and EPD datasets demonstrate that the proposed model achieves state-of-the-art performance. In the ablation study, it attains a Peak Signal-to-Noise Ratio (PSNR) of 24.406, a Structural Similarity Index (SSIM) of 0.8837, and a Character Recognition Accuracy (CRA) of 89.81%. In the comparative evaluation, the proposed model consistently outperforms the second-best comparison model across three difficulty levels, yielding approximately a 1% improvement in PSNR, a 0.8% improvement in SSIM, and an 8% improvement in CRA. This confirms the proposed model’s superiority over mainstream comparative models in restoring text fidelity and improving recognition rates. Full article