Search Results (121)

Laser beam powder bed fusion of metals (PBF-LB/M, or simply L-PBF) has emerged as one of the most competitive additive manufacturing technologies for producing complex metallic components with high precision, design freedom, and minimal material waste. Among the various categories of additive manufacturing processes, L-PBF stands out, paving the way for the execution of part designs with geometries previously considered unfeasible. Despite offering several advantages, parts with overhang features require the use of support structures to provide dimensional stability of the part. Support structures achieve this by resisting residual stresses generated during processing and assisting heat dissipation. Although the scientific community acknowledges the role of support structures in the success of L-PBF manufacturing, they have remained relatively underexplored in the literature. In this context, the present work investigated the impact of laser power and scanning speed on the dimensioning, integrity and tensile strength of single-walled block type support structures manufactured in AISI 316L stainless steel. The method proposed in this work is divided in two stages: processing parameter exploration, and mechanical characterization. The results indicated that support structures become more robust and resistant as laser power increases, and the opposite effect is observed with an increment in scanning speed. In addition, defects were detected at the interfaces between the bulk and support regions, which were crucial for the failure of the tensile test specimens. For a layer thickness corresponding to 0.060 mm, it was verified that the combination of laser power and scanning speed of 150 W and 500 mm/s resulted in the highest tensile resistance while respecting the dimensional deviation requirement. Full article

The encapsulation of hydrophobic substances remains a significant challenge due to limitations such as low loading efficiency, leakage, and poor distribution within microcapsules. This study introduces a novel strategy utilizing colloidosomes assembled from polyelectrolyte microcapsules (PMCs). PMCs were fabricated via layer-by-layer (LbL) assembly on manganese carbonate (MnCO₃) or calcium carbonate (CaCO₃) cores, followed by core dissolution. A solvent gradient replacement method was employed to substitute the internal aqueous phase of PMCs with kerosene, enabling the formation of colloidosomes through self-assembly upon resuspension in water. Comparative analysis revealed that MnCO₃-based PMCs with smaller diameters (2.5–3 µm vs. 4.5–5.5 µm for CaCO₃) exhibited 3.5-fold greater stability, attributed to enhanced inter-capsule interactions via electrostatic and hydrophobic forces. Confocal microscopy confirmed the structural integrity of colloidosomes, featuring a liquid kerosene core encapsulated within a PMC shell. Temporal stability studies indicated structural degradation within 30 min, though 5% of colloidosomes retained integrity post-water evaporation. PMC-based colloidosomes exhibit significant application potential due to their integration of colloidosome functionality with PMC-derived structural features—semi-permeability, tunable shell thickness/composition, and stimuli-responsive behavior—enabling their adaptability to diverse technological and biomedical contexts. This innovation holds promise for applications in drug delivery, agrochemicals, and environmental technologies, where controlled release and stability are critical. The findings highlight the role of core material selection and solvent engineering in optimizing colloidosome performance, paving the way for advanced encapsulation systems. Full article

Wi-Fi fingerprinting is a widely adopted technique for indoor localization in location-based services (LBS) due to its cost-effectiveness and ease of deployment using existing infrastructure. However, the performance of these systems often suffers due to missing received signal strength indicator (RSSI) measurements, which can arise from complex indoor structures, device limitations, or user mobility, leading to incomplete and unreliable fingerprint data. To address this critical issue, we propose Feature-level Augmentation for Localization (FALoc), a novel framework that enhances Wi-Fi fingerprinting-based localization through targeted feature-level data augmentation. FALoc uniquely models the observation probabilities of RSSI signals by constructing a bipartite graph between reference points and access points, which is then processed by a variational graph auto-encoder (VGAE). Based on these learned probabilities, FALoc intelligently imputes likely missing RSSI values or removes unreliable ones, effectively enriching the training data. We evaluated FALoc using an MLP (Multi-Layer Perceptron)-based localization model on the UJIIndoorLoc and UTSIndoorLoc datasets. The experimental results demonstrate that FALoc significantly improves localization accuracy, achieving mean localization errors of 7.137 m on UJIIndoorLoc and 7.138 m on UTSIndoorLoc, which represent improvements of approximately 12.9% and 8.6% over the respective MLP baselines (8.191 m and 7.808 m), highlighting the efficacy of our approach in handling missing data. Full article

A new control system implemented with a single-stage DC-DC controller to power an LED headlamp for automotive applications is presented in this work. Daytime running light (DRL), low beam (LB), high beam (HB) and adaptive driving beam (ADB) are typical functions requiring a dedicated LED driver solution to fulfill car maker requirements for front-light applications. Single-stage drivers often exhibit a significant overshoot in LED current during transitions from driving a higher number of LEDs to a lower number. To maintain LED reliability, this current overshoot must remain below the maximum current rating of the LEDs. If the overshoot overcomes this limit, it can cause permanent damage to the LEDs or reduce their lifespan. To preserve LED reliability, a comprehensive system has been proposed to minimize the peak of LED current overshoots, especially during transitions between different operating modes or LED string configurations. A key feature of the proposed system is the implementation of a parallel discharging path to be activated only when the current flowing in the LEDs is higher than a predefined threshold. A prototype incorporating an integrated test chip has been developed to validate this approach. Measurement results and comparison with state-of-the-art solutions available in the market are shown. Furthermore, a critical aspect to be considered is the proper dimensioning of the discharging path. It requires careful considerations about the gate driver capabilities, the discharging resistor values, and the thermal management of the dumping element. For this purpose, an extensive study on how to size the relative components is also presented. Full article

Lyme borreliosis (LB), caused by Borrelia burgdorferi sensu lato (s.l.) and babesiosis, caused by Babesia divergens and Babesia venatorum, are both transmitted by the ixodid tick Ixodes ricinus. Although these diseases coexist in Spain and other European regions, no studies have been carried out to assess the impact on the health of patients exposed to both causative pathogens. This retrospective study, conducted in Asturias in northwestern Spain between 2015 and 2017, analyzed the possible complications arising from exposure to these pathogens. To this end, the epidemiological, clinical and analytical features of two groups of a cohort of 120 LB patients exposed to one or two of the pathogens were compared. The cohort comprised 73 patients who had only been infected with Bo. burgdorferi s.l. (Bb group) and 47 patients who were seropositive for Ba. divergens/venatorum in addition to being infected with Bo. burgdorferi s.l. (BbBdv group). The results showed that LB patients who had also been exposed to infection with Babesia spp. (BbBdv group) had significantly more cardiorespiratory symptoms, in particular dyspnea and first- and second-degree atrioventricular (AV) blocks, compared to those who had been infected with Bo. burgdorferi s.l. alone (Bb group). No relevant differences in other symptoms, epidemiological factors or analytical tests were observed between the two groups. Full article

Bending fatigue significantly affects the mechanical stability and lifespan of biomedical implants, such as bone plates and orthopaedic fixation devices, which undergo cyclic loading in the human body. This study examines the microstructure, porosity, and bending fatigue properties of PBF-LB/M SS316L. Samples were analysed across three faces (top, front, and side) using optical microscopy (OM) and scanning electron microscopy (SEM) to observe microstructural features and porosity. Elemental composition was measured by energy-dispersive X-ray spectroscopy (EDX). Phase structures and grain orientations were characterised via X-ray diffraction (XRD) and electron backscatter diffraction (EBSD). Four-point bending fatigue tests, conducted under two loading conditions, below and slightly above the yield point, demonstrated that defects inherent to the PBF-LB/M process, particularly micropores and unmelted powder particles, strongly influence fatigue crack initiation. Real-time monitoring of crack initiation and propagation on the external sample surface was performed using a high-speed digital microscope. These findings indicate the influence of microstructural defects on fatigue performance in PBF-LB/M SS316L, supporting the design and development of more reliable patient-specific biomedical implants. Full article

Numerous existing methods demonstrate impressive performance in brightening low-illumination images but fail in detail enhancement and color correction. To tackle these challenges, this paper proposes a dual-branch network including three main parts: color space transformation, a color correction network (CC-Net), and a light-boosting network (LB-Net). Specifically, we first transfer the input into the CIELAB color space to extract luminosity and color components. Afterward, we employ LB-Net to effectively explore multiscale features via a carefully designed large–small-scale structure, which can adaptively adjust the brightness of the input images. And we use CC-Net, a U-shaped network, to generate noise-free images with vivid color. Additionally, an efficient feature interaction module is introduced for the interaction of the two branches’ information. Extensive experiments on low-light image enhancement public benchmarks demonstrate that our method outperforms state-of-the-art methods in restoring the quality of low-light images. Furthermore, experiments further indicate that our method significantly enhances performance in object detection under low-light conditions. Full article

As sixth-generation mobile communication (6G) advances towards ultra-high speed and global coverage, visible light communication (VLC) has emerged as a crucial complementary technology due to its ultra-high bandwidth, low power consumption, and immunity to electromagnetic interference. Modulation format recognition (MFR) plays a vital role in the dynamic optimization and adaptive transmission of VLC systems, significantly influencing communication performance in complex channel environments. This paper systematically reviews the research progress in MFR for VLC, comparing the theoretical frameworks and limitations of traditional likelihood-based (LB) and feature-based (FB) methods. It also explores the advancements brought by deep learning (DL) technology, particularly in enhancing noise robustness, classification accuracy, and cross-scenario adaptability through automatic feature extraction and nonlinear mapping. The findings indicate that DL-based MFR substantially enhances recognition performance in intricate channels via multi-dimensional feature fusion, lightweight architectures, and meta-learning paradigms. Nonetheless, challenges remain, including high model complexity and a strong reliance on labeled data. Future research should prioritize multi-domain feature fusion, interdisciplinary collaboration, and hardware–algorithm co-optimization to develop lightweight, high-precision, and real-time MFR technologies that align with the 6G vision of space–air–ground–sea integrated networks. Full article

For mobile agent path planning, traditional path planning algorithms frequently induce abrupt variations in path curvature and steering angles, increasing the risk of lateral tire slippage and undermining operational safety. Concurrently, conventional reinforcement learning methods struggle to converge rapidly, leading to an insufficient efficiency in planning to meet the demand for energy economy. This study proposes LSTM Bézier–Double Deep Q-Network (LB-DDQN), an advanced path-planning framework for mobile agents based on deep reinforcement learning. The architecture first enables mapless navigation through a DDQN foundation, subsequently integrates long short-term memory (LSTM) networks for the fusion of environmental features and preservation of training information, and ultimately enhances the path’s quality through redundant node elimination via an obstacle–path relationship analysis, combined with Bézier curve-based trajectory smoothing. A sensor-driven three-dimensional simulation environment featuring static obstacles was constructed using the ROS and Gazebo platforms, where LiDAR-equipped mobile agent models were trained for real-time environmental perception and strategy optimization prior to deployment on experimental vehicles. The simulation and physical implementation results reveal that LB-DDQN achieves effective collision avoidance, while demonstrating marked enhancements in critical metrics: the path’s smoothness, energy efficiency, and motion stability exhibit average improvements exceeding 50%. The framework further maintains superior safety standards and operational efficiency across diverse scenarios. Full article

Nanotopography refers to the intricate surface characteristics of materials at the sub-micron (<1000 nm) and nanometer (<100 nm) scales. These topographical surface features significantly influence the physical, chemical, and biological properties of biomaterials, affecting their interactions with cells and surrounding tissues. The development of nanostructured surfaces of polymeric nanocomposites has garnered increasing attention in the fields of tissue engineering and regenerative medicine due to their ability to modulate cellular responses and enhance tissue regeneration. Various top-down and bottom-up techniques, including nanolithography, etching, deposition, laser ablation, template-assisted synthesis, and nanografting techniques, are employed to create structured surfaces on biomaterials. Additionally, nanotopographies can be fabricated using polymeric nanocomposites, with or without the integration of organic and inorganic nanomaterials, through advanced methods such as using electrospinning, layer-by-layer (LbL) assembly, sol–gel processing, in situ polymerization, 3D printing, template-assisted methods, and spin coating. The surface topography of polymeric nanocomposite scaffolds can be tailored through the incorporation of organic nanomaterials (e.g., chitosan, dextran, alginate, collagen, polydopamine, cellulose, polypyrrole) and inorganic nanomaterials (e.g., silver, gold, titania, silica, zirconia, iron oxide). The choice of fabrication technique depends on the desired surface features, material properties, and specific biomedical applications. Nanotopographical modifications on biomaterials’ surface play a crucial role in regulating cell behavior, including adhesion, proliferation, differentiation, and migration, which are critical for tissue engineering and repair. For effective tissue regeneration, it is imperative that scaffolds closely mimic the native extracellular matrix (ECM), providing a mechanical framework and topographical cues that replicate matrix elasticity and nanoscale surface features. This ECM biomimicry is vital for responding to biochemical signaling cues, orchestrating cellular functions, metabolic processes, and subsequent tissue organization. The integration of nanotopography within scaffold matrices has emerged as a pivotal regulator in the development of next-generation biomaterials designed to regulate cellular responses for enhanced tissue repair and organization. Additionally, these scaffolds with specific surface topographies, such as grooves (linear channels that guide cell alignment), pillars (protrusions), holes/pits/dots (depressions), fibrous structures (mimicking ECM fibers), and tubular arrays (array of tubular structures), are crucial for regulating cell behavior and promoting tissue repair. This review presents recent advances in the fabrication methodologies used to engineer nanotopographical microenvironments in polymeric nanocomposite tissue scaffolds through the incorporation of nanomaterials and biomolecular functionalization. Furthermore, it discusses how these modifications influence cellular interactions and tissue regeneration. Finally, the review highlights the challenges and future perspectives in nanomaterial-mediated fabrication of nanotopographical polymeric scaffolds for tissue engineering and regenerative medicine. Full article

Objective: This prospective study aims to investigate the feasibility of Shear-Wave Elastography (SWE) for assessing the mechanical properties of the normal supraspinatus tendon and describing the elastographic features of the supraspinatus tendon under different loading conditions and positions. Methods: Twenty healthy volunteers (10 males and 10 females, aged 18–25 years) were examined by SWE using an 18-4 MHz linear array transducer. The elasticity of the supraspinatus tendon of the dominant hand was measured at three distinct locations: the insertion, middle, and myotendinous junction of the tendon. These measurements were taken under various conditions, including non-loading and the application of 5, 10, and 20 lb forces at five different positions. Results: The average elasticity was 69.2 ± 26.5 kilopascals across all positions and forces, with the middle part of the tendon exhibiting the highest elasticity (72.6 ± 6.2 kPa). An ascending trend in elasticity was observed by increasing the applied load, and the highest elasticity was observed with a 20 lb load. Determining the normal elasticity of the tendon is an important clinical implication, as understanding what is normal is essential for identifying pathological conditions. Conclusions: SWE is a feasible and promising technology for the collection of data on how the supraspinatus tendon behaves under loading conditions. There is a need for further study to better understand tendon response to activity and resultant injuries. Full article

Additive manufacturing (AM) technologies have witnessed remarkable advancements, offering opportunities to produce complex components across various industries. This paper explores the potential of AM for fabricating bipolar plates (BPPs) in fuel cell or electrolysis cell applications. BPPs play a critical role in the performance and efficiency of such cells, and conventional manufacturing methods often face limitations, particularly concerning the complexity and customization of geometries. The focus here lies in two specific AM methods: the laser powder bed fusion of metals (PBF-LB/M) and material extrusion of metals (MEX/M). PBF-LB/M, tailored for high-performance applications, enables the creation of highly complex geometries, albeit at increased costs. On the other hand, MEX/M excels in rapid prototyping, facilitating the swift production of diverse geometries for real-world testing. This approach can facilitate the evaluation of geometries suitable for mass production via sinter-based manufacturing processes. The geometric deviations of different BPPs were identified by evaluating 3D scans. The PBF-LB/M method is more suitable for small features, while the MEX/M method has lower deviations for geometrically less complex BPPs. Through this investigation, the limits of the capabilities of these AM methods became clear, knowledge that can potentially enhance the design and production of BPPs, revolutionizing the energy conversion and storage landscape and contributing to the design of additive manufacturing technologies. Full article

To date, Leucobacter species have been identified from diverse sources with various ecological and functional roles. However, the genomic features and pathogenic potential of antibiotic-resistant Leucobacter strains remain understudied. Here, we isolated the Leucobacter sp. HNU-1 from tropical Hainan Province, China, and found it can induce diapause in Caenorhabditis elegans following ingestion, while exhibiting no significant effects on the nematode’s lifespan, survival rate, locomotion, and intestinal epithelial cells. This bacterium demonstrates resistance to multiple antibiotics, including kanamycin, streptomycin, sulfonamides, and vancomycin. On LB medium, Leucobacter sp. HNU-1 forms yellow, opaque colonies with a smooth, moist surface, regular edges, a convex center, and no surrounding halo, with diameters ranging from 2 to 3 mm. Furthermore, we performed whole-genome sequencing using third-generation high-throughput sequencing technology. De novo assembly revealed a genome size of 3,375,033 bp, with a GC content of 70.37%. A total of 3270 functional genes, accounting for 88.98% of the genome, were annotated, along with six potential CRISPR sequences and other genetic elements. Genomic and bioinformatic analyses further identified antibiotics-related genes. This research provides a theoretical foundation for investigating antibiotic-resistant environmental bacteria in tropical environments and offers new insights into potential therapeutic strategies for microbial infections and host–microbe interactions. Full article

Point of Interest (POI) data play a critical role in enabling location-based services (LBS) by providing intrinsic attributes, including geographic coordinates and semantic categories, alongside a spatial context that reflects relationships among POIs. However, the inherent label sparsity in POI datasets poses significant challenges for traditional supervised learning approaches. To address this limitation, we propose MaskPOI, a novel self-supervised learning framework that combines the strengths of graph neural networks and masked modeling. MaskPOI incorporates two complementary modules: an edge mask-based graph autoencoder that models the spatial topology by predicting edge existence and uncovering hidden spatial relationships and a feature mask-based graph autoencoder that reconstructs masked node features to explore the rich attribute characteristics of POIs. Together, these modules enable MaskPOI to jointly capture the spatial and attribute information essential for robust representation learning. Extensive experiments demonstrate MaskPOI’s effectiveness in improving performance on downstream tasks such as functional zone classification and population density prediction. Ablation studies further validate the contributions of its components, highlighting MaskPOI as a powerful and versatile framework for POI representation learning. Full article

The boiling point of metals is dependent on the ambient pressure. Therefore, in laser-based fusion welding and additive manufacturing processes, the resulting process regime, ranging from heat conduction welding to the keyhole mode, is also influenced by the process pressure. While laser welding deliberately uses reduced process pressures to achieve the keyhole mode with a lower laser power input as well as a more stable keyhole, there are no positive findings on the laser powder bed fusion process (PBF-LB/M) under vacuum conditions so far. Furthermore, the literature suggests that the process window is significantly reduced, particularly in the high vacuum regime. However, this work demonstrates that components made of the ferritic steel 22NiMoCr3-7 can be successfully manufactured at low process pressures of ${2 \times 10}^{-2} \mathrm{mbar}$ using a double-scanning strategy. The strategy consists of a first scan with a defocused laser beam, where the powder is preheated and partially sintered, followed by a second scan with a slightly defocused laser beam, in which the material within a single layer is completely melted. To test this manufacturing strategy, 16 test cubes were manufactured to determine the achievable relative densities and tensile specimens were produced to assess the mechanical properties. Metallographic analysis of the test cubes revealed that relative densities of up to 98.48 ± 1.43% were achieved in the test series with 16 different process parameters. The tensile strength determined ranged from 722 to 724 MPa. Additionally, a benchmark part with complex geometric features was successfully manufactured in a high vacuum atmosphere without the need for a complex parameterization of individual part zones in the scanning strategy. Full article