Search Results (1,013)

Superhydrophobic surfaces are typically achieved through the synergistic integration of appropriate nanostructures and low-surface-energy chemical compositions. This study presents a novel and facile method for constructing a superhydrophobic hierarchical structure directly on a pristine selective laser melting (SLM) titanium surface. The intrinsic partially melted Ti particles, which are inherent to the SLM fabrication process, were strategically utilized as a natural microscale template for the in situ growth of TiO₂ nanotubes via electrochemical anodization. Three distinct micro/nano-topographies were successfully fabricated, integrating the spherical microparticles with either conventional TiO₂ nanotube arrays or separated nanotube arrays. The results demonstrate that the resulting superhydrophobic behavior can be effectively regulated by two key factors: the liquid–solid contact mode at the microscale and the strength of capillary action within the nanostructures. Notably, these characteristics can be tailored by controlling the nanotube diameter and intertubular spacing. These findings contribute to a deeper understanding of the role of micro–nano hierarchical structures in engineering superhydrophobic surfaces, thereby opening new avenues for advanced applications. Full article

Deep learning has become central to semantic segmentation of three-dimensional medical images. However—despite many published models—their adoption in practice remains limited, as deployment often requires advanced programming skills and familiarity with specific machine learning frameworks. Thus, technical barriers restrict its use to specialized users. To address this, we present SegMed (version 1.0), an open-source, standalone desktop application that provides an end-to-end workflow for deep learning-based medical image segmentation. SegMed supports the loading and inspection of common medical image formats, as well as array-based formats. The application integrates standard preprocessing operations often used in the field and directly supports loading of pretrained segmentation models implemented in both PyTorch (version 2.X) and Keras (version 2.X) and those created using the Medical Open Network for AI framework (version 1.X). Models are automatically inspected to infer required configurations, such as input size and post-processing steps, enabling segmentation with minimal user intervention. Results can be exported as volumetric images or 3D surface meshes for downstream analysis, visualization, or special applications such as virtual reality. SegMed was tested using multiple publicly available pretrained models, demonstrating robustness and flexibility across diverse segmentation tasks. By abstracting low-level implementation details, SegMed lowers technical barriers, promotes reproducibility, and facilitates the integration of AI-assisted segmentation into medical imaging workflows. Full article

Peripheral Component Interconnect Express (PCIe) is a critical interface for FPGA-based accelerators, yet existing controller solutions are often proprietary, costly, and/or incompatible with open-source workflows. We present a fully open-source PCIe controller, written in synthesizable Verilog and optimized for Field-Programmable Gate Array (FPGA) deployment. The core is verified using a Python-based cocotb_2.0.1 and pyuvm_4.0.0 testbench with a modeled Root Complex (RC), complete with data packet generation, automated checks for enumeration, flow control, and retry mechanisms. On an AMD Xilinx AC701 (XC7A200T), the design achieves less than 6% LUT utilization, timing closure at 100 MHz user clock, and demonstrates compatibility with vendor transceivers. Reference builds also meet timing on Altera Agilex devices with similar resource utilization. All RTL, verification infrastructures, and example designs are publicly released, enabling reproducible research and accelerating the development of PCIe-enabled systems for high-speed data acquisition, NVMe front-ends, and custom FPGA accelerators. Full article

Digital photoelasticity enables non-contact, full-field stress analysis through optical fringe patterns, yet its practical deployment is often constrained by experimental complexity and the limited availability of open, standardized datasets. The emergence of multi-polarizer array cameras provides polarization-resolved measurements with high information content, enabling advanced analysis strategies beyond conventional single-image approaches. This work presents a public experimental dataset composed of synchronized image sequences acquired using a polarizer array camera and a conventional RGB camera under incremental mechanical loading. The dataset comprises nine experiments, including four benchmark specimens and five bio-inspired geometries, each recorded over 720 load steps. In total, the dataset releases 25,920 polarization-resolved images and 6480 RGB images, all provided in lossless format and accompanied by experiment-specific segmentation templates. Although classical and hybrid load-stepping methods are used to demonstrate the utility of the dataset, its scope is not limited to this application. The dataset is intended as a flexible platform for exploring a wide range of photoelastic analysis techniques that leverage polarization information, while enabling direct comparison with conventional color demodulation techniques. Full article

As consumer attitudes shift, non-alcoholic and low-alcohol beers (NABLABs) have grown rapidly in popularity. This has driven interest in biological production methods that avoid the cost and flavor damage associated with post-fermentation dealcoholization. This review focuses on how barley wort composition and process conditions shape the metabolism of maltose- and maltotriose-negative non-Saccharomyces yeasts (NSYs), and how this, in turn, affects ethanol yield, flavor, and aroma in NABLABs. Key sections examine differences in carbohydrate utilization between Saccharomyces and NSYs, the influence of oxygen and Crabtree/Kluyver effects on carbon flux, and the roles of glycerol and organic acid formation as alternate carbon sinks that also contribute to mouthfeel, sweetness perception, and acidity. Particular attention is given to mashing strategies and enzyme additions used to redesign wort sugar profiles for NSYs, including high-temperature, low-gravity mashes and exogenous amyloglucosidase to increase glucose while limiting maltose and ethanol formation. The review also summarizes how the NSY-driven production of esters, higher alcohols, and the biotransformation of hop-derived precursors can offset excessive sweetness and “worty” off-flavors that commonly affect NABLABs. The use of NSYs opens an exciting array of opportunities for brewers to make NABLABs; however, challenges remain. Saccharomyces yeasts have centuries of brewing experience behind them and the adaptations needed for effective use of NSYs are still in development. Fundamentally, the challenge for NABLAB brewers using biological methods is to balance the desirable effects of fermentation while maintaining ethanol levels below the target threshold. This review outlines those challenges in detail and examines some of the approaches that are being used to solve them. Full article

High-fidelity 4D reconstruction of dynamic scenes is pivotal for immersive simulation yet remains challenging due to the photometric inconsistencies inherent in multi-view sensor arrays. Standard 3D Gaussian Splatting (3DGS) strictly adheres to the brightness constancy assumption, failing to distinguish between intrinsic scene radiance and transient brightness shifts caused by independent auto-exposure (AE), auto-white-balance (AWB), and non-linear ISP processing. This misalignment often forces the optimization process to compensate for spectral discrepancies through incorrect geometric deformation, resulting in severe temporal flickering and spatial floating artifacts. To address these limitations, we present Lumina-4DGS, a robust framework that harmonizes spatiotemporal geometry modeling with a hierarchical exposure compensation strategy. Our approach explicitly decouples photometric variations into two levels: a Global Exposure Affine Module that neutralizes sensor-specific AE/AWB fluctuations and a Multi-Scale Bilateral Grid that residually corrects spatially varying non-linearities, such as vignetting, using luminance-based guidance. Crucially, to prevent these powerful appearance modules from masking geometric flaws, we introduce a novel SSIM-Gated Optimization mechanism. This strategy dynamically gates the gradient flow to the exposure modules based on structural similarity. By ensuring that photometric enhancement is only activated when the underlying geometry is structurally reliable, we effectively prioritize geometric accuracy over photometric overfitting. Extensive experiments validate the quantitative superiority of Lumina-4DGS. On the Waymo Open Dataset, our method achieves a state-of-the-art Full Image PSNR of 31.12 dB while minimizing geometric errors to a Depth RMSE of 1.89 m and Chamfer Distance of 0.215 m. Furthermore, on our highly challenging self-collected surround-view dataset featuring severe unconstrained illumination shifts, Lumina-4DGS yields a significant 2.13 dB PSNR improvement over recent driving-scene baselines. These results confirm that our framework achieves photorealistic, exposure-invariant novel view synthesis while maintaining superior geometric consistency across heterogeneous camera inputs. Full article

For the first time, the ternary Zintl phases RbCaBi and CsCaBi have been synthesized and structurally characterized via single-crystal X-ray diffraction methods. These two compounds, alongside KCaBi, are confirmed to crystallize in a tetragonal crystal system with the space group P4/nmm (no. 129) with two formula units per cell. The lattice constants increase monotonically from a = 5.3812(10) Å and c = 8.410(3) Å for KCaBi, to a = 5.4139(7) Å and c = 8.6180(17) Å for RbCaBi, and to a = 5.4709(11) Å and c = 8.914(3) Å for CsCaBi. The crystal structure can be visualized as an array of square prisms formed of Bi atoms, which are centered by alkali metal atoms, while the Ca atoms fill tetrahedra formed of Bi atoms. There are no direct Bi–Bi interactions in the crystal structure; therefore, with full cation ordering present, the chemical bonding in the ACaBi compounds can be rationalized within the fully ionic approximation as A⁺Ca²⁺Bi³⁻ (A = K, Rb, Cs). This suggests the opening of an (narrow) energy gap between the valence and conduction bands, i.e., semiconducting behavior. Full article

This paper describes the development of several software-based games using a high-level programming language (C in our case), designed so that they can be ported to a Field-Programmable Gate Array (FPGA). It also outlines the mathematical foundations underlying these games. Making executables portable in this way can simplify running applications on FPGA platforms. Porting a game to an FPGA serves as evidence that arbitrary executables can be migrated to such hardware. The complete workflow for creating the game, along with the final game outcomes, is detailed in this paper. In addition, statistical analyses of these games were conducted. The proposed approach relies on graphics and character-handling libraries typically available in advanced programming languages. The background of this work is that a microcontroller architecture which can easily be run on a Spartan-6 FPGA was needed. The innovative point of this paper is that it created the cross-compilation toolchain on an uncommon microcontroller architecture, like the OpenRISC. Full article

Biomimetic anisotropy is defined as intentionally engineered, nature-inspired directional differences in structure, chemistry, roughness, stiffness, or pore architecture. These directional differences lower transport resistance in one direction relative to the opposite direction, which results in rectified transport. In this review, anisotropy design is synthesized across surfaces, porous materials, and soft systems, with transport considered for droplets, low-surface-tension liquids, particles, and soft objects. Biological inspirations are summarized first, and the design lessons that can be transferred to engineered platforms are then extracted. Key anisotropic architectures are classified next, including ratchets and sawtooth textures, bristle- or setae-like fibrillar arrays, grooves and wedges, asymmetric pores and membranes, chemically patterned surfaces, and hierarchical micro–nano combinations. Practical fabrication methods and material choices are reviewed thereafter, spanning micro- and nanofabrication, additive manufacturing, coatings and surface modification, and responsive soft matter. The field is then organized mechanistically around how anisotropy generates directionality through contact-line pinning asymmetry, curvature-driven capillary pressure bias, compliance and elastocapillary coupling, and active rectification under oscillatory forcing. Finally, these mechanisms are connected to application needs in pump-free microfluidics and sampling, long-distance open transport, environmental water management, and fouling-prone self-cleaning systems. Throughout the review, design-to-function links are emphasized, and open challenges are highlighted, including durability under real fluids and contaminants as well as scalable manufacturing and integration. Full article

Standard maturity methods for concrete monitoring rely primarily on temperature history, often neglecting the influence of internal relative humidity (RH) on hydration kinetics and self-desiccation risks. Continuous in situ monitoring of internal RH remains a challenge due to the high cost, proprietary nature, and lack of reproducibility of existing solutions. This study evaluates a low-cost, open-source embedded sensor array designed to characterize early-age curing behavior through trend-based monitoring—defined here as the evaluation of ensemble consistency and repeatability rather than absolute metrological traceability. The prototype system, based on SHT31 sensors controlled by an ESP32 microcontroller, was embedded in high-performance concrete cylinders (f′c = 45 MPa) to capture the exothermic hydration peak and the equilibration of internal humidity. Results demonstrate that while the sensor encapsulation introduced a geometric disturbance that reduced compressive strength by approximately 25%—a limitation requiring mitigation in structural applications—the system successfully captured reproducible curing transitions. The proposed framework provides an accessible tool for experimental research into internal curing conditions, offering a digital complement to traditional surface-based quality control. Full article

The synthesis of continuous non-linear metal nanostructures at the micro and nanoscale remains a challenging frontier in nanotechnology due to inherent synthetic constraints. This study introduces an innovative chemical methodology for fabricating continuous rings and diverse geometries via the chemical fusion of gold nanorods (AuNRs) on a solid substrate. Initially, aqueous solutions of cetyltrimethylammonium bromide (CTAB)-coated AuNRs were deposited and dried on a solid substrate, resulting in the self-assembly of ring-like arrays. Subsequent chemical growth of the AuNRs in all dimensions was achieved using an aqueous solution of Au(I)/CTAB/Ascorbic Acid (AA), enabling their fusion into continuous structures. This approach permits the formation of arbitrary shapes by pre-arranging AuNRs, thereby opening new avenues for the exploration of non-linear nanostructures with potentially novel plasmonic and electronic properties. The capability to engineer such complex nanostructures is pivotal for advancing fields such as photonics, electronics, and sensing, where the unique optical and electronic properties of gold nanostructures can be exploited for cutting-edge applications. Furthermore, this technique shows a significant promise for the fabrication of various micro- and nanodevices and the seamless interconnection of components in integrated electronic circuits, potentially leading to more efficient and miniaturized electronic systems. The broader implications of this research are significant, offering a potential pathway to the development of nanomaterials and devices that could benefit various industries and technological processes. Full article

Vat polymerization-based 3D printing has emerged as a promising approach for the rapid, low-cost, and scalable fabrication of microfluidic devices; however, achieving high-resolution and fully clog-free microchannels using commercial resins remains challenging. In this study, we systematically investigate key printing parameters—including channel orientation, length, layer thickness, and exposure time—to elucidate their effects on channel openness, dimensional fidelity, and surface morphology using a commercially available low-cost masked stereolithography (MSLA) printer and printing resin, thereby establishing quantitative fabrication boundaries that define the transition from fully open to blocked microchannels in practice. Under optimized printing conditions, microchannels with characteristic dimensions exceeding 200 µm were fabricated in a reliable and clog-free manner using standard commercial resins. In addition, by implementing a size-compensated design strategy, we achieved the fabrication of complex droplet generator arrays with a minimum central channel width of 400 µm, while maintaining an internal dimensional deviation below 2.5%. These investigations significantly expand the practical applicability of low-cost MSLA 3D printing for microfluidic device fabrication, providing a scalable and accessible pathway for producing high-fidelity microchannels without reliance on custom resins or post-processing-intensive workflows. Full article

Accurate and rapid prediction of sensor responses is critical for real-time measurement systems, digital twin implementations, and sensor design optimization. Traditional numerical methods such as Finite Element Method (FEM) and Computational Fluid Dynamics (CFD) provide high-fidelity solutions but suffer from prohibitive computational costs, limiting their applicability in time-sensitive applications. This paper presents a novel framework utilizing Fourier Neural Operators (FNO) as surrogate models for fast multi-physics sensor response prediction across thermal, acoustic, and flow measurement domains. Unlike conventional neural networks that learn finite-dimensional mappings, FNO learns operators between infinite-dimensional function spaces by parameterizing the integral kernel in Fourier space, enabling resolution-invariant predictions with remarkable computational efficiency. We demonstrate the framework’s efficacy through three comprehensive case studies: (1) thermal sensor response prediction achieving $R^2>0.98$ with 8300× speedup over FEM, (2) acoustic sensor array modeling with mean absolute error below 0.5 dB and 4000× speedup over BEM, and (3) flow sensor characterization with velocity field prediction accuracy exceeding 97% and 31,000× speedup over CFD. The proposed FNO-based surrogate models are trained on simulation datasets generated from high-fidelity numerical solvers and validated against simulation holdout data for all three case studies, with additional experimental validation conducted for the thermal sensor case. Results indicate that FNO architectures effectively capture the underlying physics governing sensor behavior while reducing inference time from minutes to milliseconds. The framework enables real-time sensor calibration, uncertainty quantification, and design optimization, opening new possibilities for intelligent measurement systems and Industry 4.0 applications. We also investigate the spectral characteristics of FNO predictions, addressing the inherent low-frequency bias through a hybrid architecture combining FNO with local convolutional layers. The primary contributions of this work include: (1) the first systematic application of FNO-based surrogate modeling specifically tailored for sensor response prediction across multiple physics domains, (2) a novel H-FNO architecture that combines spectral operators with local convolutions to mitigate spectral bias in sensor applications, and (3) comprehensive validation including both simulation and experimental data for practical deployment. This work establishes FNO as a powerful tool for accelerating sensor simulation and advancing the field of AI-enhanced instrumentation and measurement. Full article

In low-amplitude and low-frequency vibration environments, the energy harvesting efficiency of self-powered wireless sensor nodes is insufficient, limiting their long-term autonomous operation. To address this issue, a micro piezoelectric–electromagnetic hybrid energy harvester is designed, aiming to enhance energy capture efficiency through structural integration and parameter optimization. The study is conducted entirely through numerical simulations. A coaxial integrated architecture is adopted, combining a piezoelectric cantilever beam array with an electromagnetic induction module. The piezoelectric layer uses lead magnesium niobate–lead titanate (PMN-PT) solid solution material with a thickness of 0.2 mm. The electromagnetic module employs copper wire coils with a diameter of 0.08 mm, winding 1500–3000 turns, paired with N52-type neodymium–iron–boron (NdFeB) permanent magnets. To improve energy conversion efficiency, the optimization parameters include the length-to-thickness ratio of the cantilever beam, the mass of the tip mass, the number of coil turns, and the spacing of the permanent magnets. Each parameter is set at four levels for orthogonal experiments. A multi-physics coupling model is established using ANSYS Workbench 2023, covering structural dynamics, piezoelectric effects, and the electromagnetic induction module. The mesh size is set to 0.1 mm. The energy output characteristics are analyzed under vibration frequencies of 0.3–12 Hz and amplitudes of 0.2–1.0 mm. Simulation results show that the optimized hybrid harvester achieves 45% higher energy conversion efficiency than a single piezoelectric structure and 31% higher than a traditional separated hybrid structure within the 0.3–12 Hz low-frequency range. Under a 6 Hz frequency and 0.6 mm amplitude, the output power density reaches 3.5 mW/cm³, the peak open-circuit voltage is 4.1 V, and the peak short-circuit current is 1.3 mA. Under environmental conditions of 20–88% humidity and −15–65 °C temperature, the device maintains over 94% stability in energy output. After 1.2 million vibration cycles, structural integrity remains above 96%, and energy conversion efficiency decreases by no more than 5%. The proposed coaxial hybrid structure and multi-parameter orthogonal optimization method effectively enhance energy harvesting performance in low-amplitude, low-frequency environments. The simulation design parameters and analysis procedures provide a reference for the development of similar micro hybrid energy harvesters and support the performance optimization of self-powered wireless sensor nodes. Full article

Food allergy is an increasing global health burden, particularly in industrialized countries, with rising prevalence in both pediatric and adult populations. It is characterized by exaggerated immune responses to innocuous dietary antigens, leading to clinical manifestations ranging from mild gastrointestinal symptoms to life-threatening anaphylaxis. Mast cells are central effectors in the pathophysiology of food allergy, initiating and amplifying allergic inflammation through the release of a broad array of mediators upon activation. Recent studies have revealed that the intestinal microbiota plays a critical role in shaping immune responses, including the regulation of mast cell development, maturation, and activation. Moreover, dysbiosis has been associated with increased susceptibility to allergic sensitization and heightened mast cell reactivity. This review explores the molecular mechanisms underlying the microbiota–mast cell axis in the context of intestinal homeostasis and food allergy with a particular emphasis on the regulation of mast cell effector functions by TLR signaling and microbial metabolites. We also discuss the therapeutic potential of targeting the microbiota–mast cell axis as novel strategies to restore immune tolerance. Understanding this complex crosstalk opens new avenues for translational approaches in the prevention and treatment of food allergy. Full article