Search Results (2,233)

Current systems for exchanging medical records struggle with efficiency and privacy issues. While establishing the Electronic Medical Record Exchange Center (EEC) in 2012 was intended to alleviate these issues, its centralized structure has brought about new attack vectors, such as performance bottlenecks, single points of failure, and an absence of patient consent over their data. Methods: This paper describes a novel EMR Gateway system that uses blockchain technology to exchange electronic medical records electronically, overcome the limitations of current centralized systems for sharing EMR, and leverage decentralization to enhance resilience, data privacy, and patient autonomy. Our proposed system is built on two interconnected blockchains: a Decentralized Identity Blockchain (DID-Chain) based on Ethereum for managing user identities via smart contracts, and an Electronic Medical Record Blockchain (EMR-Chain) implemented on Hyperledger Fabric to handle medical record indexes and fine-grained access control. To address the dual requirements of cross-platform data exchange and patient privacy, the system was developed based on the Fast Healthcare Interoperability Resources (FHIR) standard, incorporating stringent de-identification protocols. Our system is built using the FHIR standard. Think of it as a common language that lets different healthcare systems talk to each other without confusion. Plus, we are very serious about patient privacy and remove all personal details from the data to keep it confidential. When we tested its performance, the system handled things well. It can take in about 40 transactions every second and pull out data faster, at around 49 per second. To give you some perspective, this is far more than what the average hospital in Taiwan dealt with back in 2018. This shows our system is very solid and more than ready to handle even bigger workloads in the future. Full article

With the advancement of intelligent technology and the rise in labor costs, manual identification and cutting of 3D cotton cup indentations can no longer meet modern demands. The increasing variety and shape of 3D cotton cups due to personalized requirements make the use of fixed molds for cutting inefficient, leading to a large number of molds and high costs. Therefore, this paper proposes a UNet-based indentation segmentation algorithm to automatically extract 3D cotton cup indentation data. By incorporating the VGG16 network and Leaky-ReLU activation function into the UNet model, the method improves the model’s generalization capability, convergence speed, detection speed, and reduces the risk of overfitting. Additionally, attention mechanisms and an Atrous Spatial Pyramid Pooling (ASPP) module are introduced to enhance feature extraction, improving the network’s spatial feature extraction ability. Experiments conducted on a self-made 3D cotton cup dataset demonstrate a precision of 99.53%, a recall of 99.69%, a mIoU of 99.18%, and an mPA of 99.73%, meeting practical application requirements. The extracted 3D cotton cup indentation contour data is automatically input into an intelligent CNC cutting machine to cut 3D cotton cup. The cutting results of 400 data points show an 0.20 mm ± 0.42 mm error, meeting the cutting accuracy requirements for flexible material 3D cotton cups. This study may serve as a reference for machine vision, image segmentation, improvements to deep learning architectures, and automated cutting machinery for flexible materials such as fabrics. Full article

This study presents a comparative evaluation of post-process sensor integration in additively manufactured (AM) metal and the in-situ process for polymer structures for structural health monitoring (SHM), with an emphasis on embedded sensors. Geometrically identical specimens were fabricated using copper via metal fused filament fabrication (FFF) and PLA via polymer FFF, with piezoelectric transducers (PZTs) inserted into internal cavities to assess the influence of material and placement on sensing fidelity. Mechanical testing under compressive and point loads generated signals that were transformed into time–frequency spectrograms using a Short-Time Fourier Transform (STFT) framework. An engineered RGB representation was developed, combining global amplitude scaling with an amplitude-envelope encoding to enhance contrast and highlight subtle wave features. These spectrograms served as inputs to convolutional neural networks (CNNs) for classification of load conditions and detection of damage-related features. Results showed reliable recognition in both copper and PLA specimens, with CNN classification accuracies exceeding 95%. Embedded PZTs were especially effective in PLA, where signal damping and environmental sensitivity often hinder surface-mounted sensors. This work demonstrates the advantages of embedded sensing in AM structures, particularly when paired with spectrogram-based feature engineering and CNN modeling, advancing real-time SHM for aerospace, energy, and defense applications. Full article

Thermoset fibre-reinforced composites are widely used in high-end industries, but a growing demand for more sustainable and recyclable alternatives conveyed the research efforts towards thermoplastics. To expand their usage, new approaches to their manufacture and mechanical performance must be tackled and tailored to each engineering challenge. The present study designed, manufactured and tested advanced multi-layer laminated composites of thermoplastic polypropylene prepreg reinforced with continuous woven fibreglass with interlayer toughening through thermoplastic polyurethane elastomer (TPU) layers manufactured by fused filament fabrication. The manufacturing process was iteratively optimized, resulting in successful adhesion between layers. Three composite configurations were produced: baseline glass fibre polypropylene (GFPP) prepreg and two multi-layer composites, with solid and honeycomb structured TPU layers. Thermal and mechanical analyses were conducted with both the polyurethane elastomer and the manufactured laminates. Tensile testing was conducted on additively manufactured polyurethane elastomer specimens, while laminated composites were tested in three-point bending. The results demonstrated the potential of the developed laminates. TPU multi-layer laminates exhibit higher thermal stability compared to the baseline GFPP prepreg-based composites. The addition of elastomeric layers decreases the flexural modulus but increases the ability to sustain plastic deformation. Multi-layer laminate composites presenting honeycomb TPU layers exhibit improved geometric and mechanical consistency, lower delamination and fibre breakage, and a high elastic recoverability after testing. Full article

This study aimed to evaluate the scientific reliability of 3D-printed silicone boluses fabricated with patient-specific molds, focusing on fabrication-related uncertainties such as internal air bubbles, thickness variations, and density differences, thereby providing evidence for clinical quality assurance. Custom silicone boluses were fabricated using 3D-printed molds with varying vacuum degassing times (1, 5, and 10 min). Air bubble size and depth were quantified using scanner image analysis, while density and Hounsfield unit (HU) values were compared with a commercial bolus. Dosimetric evaluation was performed using a VitalBeam linear accelerator (6 MV photons, Varian Medical Systems, Palo Alto, CA, USA) and a MatriXX 2D detector (IBA Dosimetry, Schwarzenbruck, Germany), comparing treatment planning system (TPS) calculated doses with measured doses across a 3 × 3 grid. Surface dose distributions were further analyzed using EBT3 film. Results showed that bubble size increased with longer vacuum times, interpreted as coalescence due to limited degassing and silicone viscosity. The density of 3D boluses ranged from 0.980 to 1.104 g/cm³ (commercial: 0.988 g/cm³), with HU values of +240 to +250 (commercial: −110). In point-wise comparisons, mean dose differences were less than 1% for 1- and 5 min samples and approximately 1% for 10 min, with all conditions within |Δ| ≤ 3%. Film analysis confirmed equivalent surface dose distributions. These findings demonstrate, for the first time, that microscopic bubbles in 3D-printed silicone boluses have negligible clinical impact, supporting their safe adoption without requiring complex degassing procedures. Full article

Titanium (Ti)-based implants are widely used for bone injuries but suffer from poor bioactivity. To address this, we propose an innovative synergistic approach that combines laser melting deposition (LMD) for the fabrication of titanium-based supports with laser direct writing via two-photon polymerization (LDW via TPP) for their functionalization with 3D polymeric microstructures. We functionalized Ti surfaces fabricated by LMD using Ti (99.85 wt.%) and TiC powders (79.95 wt.% Ti, 20.05 wt.% C), with 3D microstructures obtained by LDW via TPP. The 3D microstructures were made of IP-Dip photopolymer and comprised 64 vertical microtubes arranged in five layers (10 to 170 μm tall, >94% porosity). When seeded with MG-63 osteoblast-like cells, the Ti-based surfaces functionalized with 3D polymeric microstructures promoted 3D cells’ spatial organization. Moreover, the cells seeded on functionalized Ti-based surfaces showed earlier organic matrix synthesis (day 7 vs. day 14) and mineralization (higher deposits of calcium and phosphorus, starting from day 7), as compared with the cells from non-functionalized Ti. In addition, the traction forces exerted by the cells on the 3D microstructures, determined using FEBio Studio software, were of the order of hundreds of µN, whereas if the cells would have been seeded on extracellular matrix-like materials, the traction forces would have been of only few nN. These results point towards the major role played by 3D polymeric microarchitectures in the interaction between osteoblast-like cells and Ti-based surfaces. Overall, the functionalization of Ti-based constructs fabricated by LMD with 3D polymeric microstructures made by LDW via TPP significantly improved Ti bioactivity. Full article

Acoustic spiral wavefronts demonstrate linear phase directionality, facilitating precise azimuth estimation in underwater navigation through phase comparison with reference wavefronts characterized by constant phase directionality. The reliability of azimuth estimation depends on the phase directionality accuracy of both the spiral and reference wavefront sources. This study introduces a seven-element transmitting array, constructed using bender disk transducers, which is capable of generating both spiral and reference acoustic wavefronts with minimal phase directionality error. The array design was developed and evaluated using a point source array model and numerical simulations, followed by physical fabrication. To address the sensitivity of the phase–azimuth linearity to manufacturing imperfections in sound sources, a phase error compensation technique was implemented by adjusting the input signal parameters to the acoustic emitters. Experimental validation was conducted in an anechoic water tank, where both spiral and reference wavefronts were transmitted across multiple frequencies. The results reveal that the array prototype achieved sub-degree-level compensated phase directionality accuracy for both wavefront types at all the tested frequencies. Notably, at the resonance frequency of 7.3 kHz, the root-mean-square phase directionality error of the spiral wavefront was reduced to as low as 0.19°. Full article

In response to the demand for rapid matte finishing on large-format 304 stainless steel surfaces, this study utilized four fiber laser devices (output wavelength: 1064 nm, output power: 100 W, maximum modulation frequency: 4 kHz) to simultaneously perform surface matte finishing experiments on 304 stainless steel, with the aim of fabricating anti-reflective micro-nano structures. During the experiments, by systematically investigating the influence of parameters—including laser power, scanning speed, frequency, and idle speed of a single laser head—on the matte finishing process, the optimal processing parameters for a single laser head were determined as follows: laser power of 20 W, scanning speed of 11,000 mm/s, and frequency of 80 kHz. For large-area high-speed laser matte finishing, the delay of laser on/off was adjusted to compensate for the galvanometer’s motion trajectory, thereby ensuring uniform ablation at both the start and end positions of the processing path. Furthermore, in the context of large-area rapid multi-head laser matte finishing on 304 stainless steel, the overlapping of surface regions processed by different galvanometers was achieved by calibrating the motion start and end points of each galvanometer. The optimal overlapping parameters were successfully obtained. This study provides technical support for environmentally friendly matte finishing of stainless steel and offers valuable insights for its application in the stainless steel home appliance industry. 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

Understanding the fatigue behavior of materials is essential for designing components capable of enduring prolonged use under varying stress conditions. This study investigates the high-cycle fatigue and fatigue crack growth characteristics of X17CrNi15-2 stainless steel. Very high-cycle fatigue (VHCF) and fatigue crack growth tests were conducted on conventional fatigue and compact tension (CT) specimens fabricated from X17CrNi15-2 stainless steel. The fatigue crack growth behavior of the CT specimens was analyzed using Paris’ law. A revised version of Paris’ law was suggested based on the fatigue crack growth rate plotted against the stress intensity factor range, expanding on prior research utilizing three-point single-edge notch bend specimens. Scanning electron microscopy (SEM) was employed to examine the fracture mechanisms of both fatigue specimen types. The results indicated that the fatigue specimens failed in the VHCF regime under stress amplitudes ranging from 100 to 450 MPa. A power law correlation between stress amplitude and fatigue life was established, with material constants of 7670.3954 and −0.1663. These findings offer valuable insights into the material’s performance and are crucial for enhancing its suitability in engineering applications where high-cycle fatigue is a critical factor. Full article

Tryptophan (Trp) and tryptamine (Tryp), critical biomarkers in mood regulation, immune function, and metabolic homeostasis, are increasingly recognized for their roles in both oral and systemic pathologies, including neurodegenerative disorders, cancers, and inflammatory conditions. Their rapid, sensitive detection in biofluids such as saliva—a non-invasive, real-time diagnostic medium—offers transformative potential for early disease identification and personalized health monitoring. This review synthesizes advancements in electrochemical sensor technologies tailored for Trp and Tryp quantification, emphasizing their clinical relevance in diagnosing conditions like oral squamous cell carcinoma (OSCC), Alzheimer’s disease (AD), and breast cancer, where dysregulated Trp metabolism reflects immune dysfunction or tumor progression. Electrochemical platforms have overcome the limitations of conventional techniques (e.g., enzyme-linked immunosorbent assays (ELISA) and mass spectrometry) by integrating innovative nanomaterials and smart engineering strategies. Carbon-based architectures, such as graphene (Gr) and carbon nanotubes (CNTs) functionalized with metal nanoparticles (Ni and Co) or nitrogen dopants, amplify electron transfer kinetics and catalytic activity, achieving sub-nanomolar detection limits. Synergies between doping and advanced functionalization—via aptamers (Apt), molecularly imprinted polymers (MIPs), or metal-oxide hybrids—impart exceptional selectivity, enabling the precise discrimination of Trp and Tryp in complex matrices like saliva. Mechanistically, redox reactions at the indole ring are optimized through tailored electrode interfaces, which enhance reaction kinetics and stability over repeated cycles. Translational strides include 3D-printed microfluidics and wearable sensors for continuous intraoral health surveillance, demonstrating clinical utility in detecting elevated Trp levels in OSCC and breast cancer. These platforms align with point-of-care (POC) needs through rapid response times, minimal fouling, and compatibility with scalable fabrication. However, challenges persist in standardizing saliva collection, mitigating matrix interference, and validating biomarkers across diverse populations. Emerging solutions, such as AI-driven analytics and antifouling coatings, coupled with interdisciplinary efforts to refine device integration and manufacturing, are critical to bridging these gaps. By harmonizing material innovation with clinical insights, electrochemical sensors promise to revolutionize precision medicine, offering cost-effective, real-time diagnostics for both localized oral pathologies and systemic diseases. As the field advances, addressing stability and scalability barriers will unlock the full potential of these technologies, transforming them into indispensable tools for early intervention and tailored therapeutic monitoring in global healthcare. Full article

Biosensors have undergone transformative advancements, evolving into sophisticated wearable and implantable devices capable of real-time health monitoring. Traditional manufacturing methods, however, face limitations in scalability, cost, and design complexity, particularly for miniaturized, multifunctional biosensors. The integration of 3D printing technology addresses these challenges by enabling rapid prototyping, customization, and the production of intricate geometries with high precision. This review explores how additive manufacturing techniques facilitate the fabrication of flexible, stretchable, and biocompatible biosensors. By incorporating advanced materials like conductive polymers, nanocomposites, and hydrogels, 3D-printed biosensors achieve enhanced sensitivity, durability, and seamless integration with biological systems. Innovations such as biodegradable substrates and multi-material printing further expand applications in continuous glucose monitoring, neural interfaces, and point-of-care diagnostics. Despite challenges in material optimization and regulatory standardization, the convergence of 3D printing with nanotechnology and smart diagnostics heralds a new era of personalized, proactive healthcare, offering scalable solutions for both clinical and remote settings. This synthesis underscores the pivotal role of additive manufacturing in advancing wearable and implantable biosensor technology, paving the way for next-generation devices that prioritize patient-specific care and real-time health management. Full article

This paper introduces a dual-band planar microwave sensor system for measuring the complex dielectric constant of solid material. The sensor system comprises three constituent parts: the sensing probe, the circuit module and the broadband coupler. The sensing probe is composed of a host part and a sensing area. The host part is composed of a microstrip line, which facilitates system integration with other planar microwave components. The sensing area comprises two pairs of E-interdigital structures, which were originally developed from the interdigital capacitor. This configuration manifests two resonant frequency points, specifically 3 GHz and 3.92 GHz. Consequently, any environmental effects exhibit equivalent variation at both resonant frequency points, thereby substantiating the efficacy of the proposed sensor system for differential operation, which has the capacity to mitigate the impact of environmental conditions. The circuit module comprises a controller, two detectors and a signal generator, which facilitate the generation and processing of radio frequency signals within the system. The function of the broadband coupler is to differentiate between the incident signal and the reflected signal. The operating principle is predicated on the variation in the resonant frequency and peak attenuation with respect to the complex dielectric constant of the material under test (MUT). In order to validate the effectiveness of the proposed sensor system, a prototype is fabricated and tested. The proposed sensor system is distinguished by its high sensitivity and low cost. The apparatus is capable of performing measurements independently and without the necessity for auxiliary equipment. Full article

Three-dimensional printing technology is fundamentally reshaping the design and fabrication of health monitoring sensors. While it holds great promise for achieving miniaturization, multi-material integration, and personalized customization, the lack of a clear selection framework hinders the optimal matching of printing technologies to specific sensor requirements. This review presents a classification framework based on existing standards and specifically designed to address sensor-related requirements, categorizing 3D printing technologies into point-based, line-based, and area-based modalities according to their fundamental fabrication unit. This framework directly bridges the capabilities of each modality, such as nanoscale resolution, multi-material versatility, and high-throughput production, with the critical demands of modern health monitoring sensors. We systematically demonstrate how this approach guides technology selection: Point-based methods (e.g., stereolithography, inkjet) enable micron-scale features for ultra-sensitive detection; line-based techniques (e.g., Direct Ink Writing, Fused Filament Fabrication) excel in multi-material integration for creating complex functional devices such as sweat-sensing patches; and area-based approaches (e.g., Digital Light Processing) facilitate rapid production of sensor arrays and intricate structures for applications like continuous glucose monitoring. The point–line–area paradigm offers a powerful heuristic for designing and manufacturing next-generation health monitoring sensors. We also discuss strategies to overcome existing challenges, including material biocompatibility and cross-scale manufacturing, through the integration of AI-driven design and stimuli-responsive materials. This framework not only clarifies the current research landscape but also accelerates the development of intelligent, personalized, and sustainable health monitoring systems. Full article

Network monitoring is becoming increasingly challenging as networks grow in scale, speed, and complexity. The evolution of monitoring approaches reflects a shift from device-centric, localized techniques toward network-wide observability enabled by modern networking paradigms. Early methods like SNMP polling and NetFlow provided basic insights but struggled with real-time visibility in large, dynamic environments. The emergence of Software-Defined Networking (SDN) introduced centralized control and a global view of network state, opening the door to more coordinated and programmable measurement strategies. More recently, programmable data planes (e.g., P4-based switches) and in-band telemetry frameworks have allowed fine grained, line rate data collection directly from traffic, reducing overhead and latency compared to traditional polling. These developments mark a move away from single point or per flow analysis toward holistic monitoring woven throughout the network fabric. In this survey, we systematically review the state of the art in network-wide monitoring. We define key concepts (topologies, flows, telemetry, observability) and trace the progression of monitoring architectures from traditional networks to SDN to fully programmable networks. We introduce a taxonomy spanning local device measures, path level techniques, global network-wide methods, and hybrid approaches. Finally, we summarize open research challenges and future directions, highlighting that modern networks demand monitoring frameworks that are not only scalable and real-time but also tightly integrated with network control and automation. Full article