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Phase-field simulation of the dendrite growth in aluminum alloy AA5754 during alternating current electromagnetic stirring laser beam welding
No full textElectromagnetic stirring is known to promote material flow, reduce porosity, uniform elements distribution, and refine grain in laser beam welding (LBW), which enhances the applicability of LBW in various industries. In this study, a phase-field model of dendrite growth in AA5754 Al alloy electromagnetic stirring laser beam welding was established. The model considered the thermal electromagnetic Lorentz force resulting from the interaction between the electric field generated by the Seebeck effect and the magnetic field, as well as the temperature gradient and solidification rate of the solidification interface obtained from the computational fluid dynamics electromagnetic stirring LBW model. The variation rules of dendrite growth with different magnetic parameters and effects are analyzed. Comprehensively, the magnetic field promotes the solidification rate, thus promoting interfacial instability and a large magnetic flux density leads to a faster interface instability. The solidification rate as well as the temperature gradient affect the growth rate, and the accelerated growth caused by the solidification rate with a high frequency and a large magnetic flux density effectively inhibits the slow growth caused by the temperature gradient. The thermal electromagnetic Lorentz force is the main factor for the branch increment at low frequencies, while both thermal electromagnetic Lorentz force and temperature gradient increase the number of branches at high frequencies. The calculated average branch numbers considering various factors in the stable stage under different magnetic parameters were consistent with the results of the scanning electron microscope tests.21
Towards Crowd-Based Requirements Engineering for Digital Farming (CrowdRE4DF)
No full text457465The farming domain has seen a tremendous shift to-wards digital solutions. However, capturing farmers' requirements regarding Digital Farming (DF) technology remains a difficult task due to domain-specific challenges. Farmers form a diverse and international crowd of practitioners who use a common pool of agricultural products and services, which means we can consider the possibility of applying Crowd-based Requirements Engineering (CrowdRE) for DF: CrowdRE4DF. We found that online user feedback in this domain is limited, necessitating a way of capturing user feedback from farmers in situ. Our solution, the Farmers' Voice application, uses speech-to-text, Machine Learning (ML), and Web 2.0 technology. A preliminary evaluation with five farmers showed good technology acceptance, and accurate transcription and ML analysis even in noisy farm settings. Our findings help to drive the development of DF technology through in-situ requirements elicitation
Continuous Coagulation Monitoring in Human Blood Samples via Magnetic Particle Spectroscopy
No full textIntroduction: Magnetic Particle Imaging (MPI) is a radiation-free imaging modality based on the nonlinear magnetic response of iron oxide nanoparticles, providing high sensitivity and real-time, quantitative, background-free imaging. With the clinical approval of Resotran as an MPI-suitable tracer and the development of first human-scale scanners, clinical applications are within reach. Magnetic Particle Spectroscopy (MPS), the non-imaging counterpart of MPI, enables sensitive analytics by exploiting the signal response of magnetic nanoparticles. In this pilot study, we prove the potential of MPS to continuously monitor blood coagulation in real time.
Methods: Blood samples from five volunteers were mixed with the commercial magnetic resonance imaging contrast agent Resotran. The dynamics of the particle signal were assessed in a custom-built MPS-system for a duration of 45 minutes under various conditions, including the presence of anticoagulants (EDTA, Heparin, Citrate) and mechanical stress. The signal amplitude of the fifth harmonic of the MPS was analyzed. To exclude potential thermal effects, the temperature inside the MPS was monitored by using a fiber optic thermometer during the measurements.
Results: All Resotran-containing blood samples showed a signal decrease over time. Samples with anticoagulants exhibited no relevant signal decrease (EDTA, Citrate) or a smaller decrease (Heparin) compared to samples without anticoagulants. Additionally, mechanical stress induced a signal decay in all samples, further indicating the link between the observed MPS signal decay and blood coagulation.
Conclusion: This study shows that continuous monitoring of human blood coagulation via MPS is feasible, making bedside coagulation monitoring in clinical settings a concrete perspective.2
Process-related challenges in the formation of SiO2 layers by chemical vapour deposition for MEMS applications
No full textWe investigate the electrical properties and reliability of silicon dioxide (SiO₂) layers deposited using low-pressure tetraethyl orthosilicate (LP-TEOS) and plasma-enhanced tetraethyl orthosilicate (PE-TEOS) methods. Capacitance-voltage (C-V) and current-voltage (I-V) measurements were performed to evaluate dielectric constant variations, leakage currents, and charge trapping mechanisms. The results show that LP-TEOS films exhibit a strong dependence on deposition temperature and gas flow rate, affecting both the flat-band voltage shift and interface state density. A flat-band voltage shift was also observed in PE-TEOS layers under varying deposition conditions, though it was less pronounced than in LP-TEOS. In both LP-TEOS and PE-TEOS layers, a significant concentration of positively charged defects was observed, except in the wafer deposited at 800 °C using the maximum precursor flow rate. Furthermore, stress measurements indicate compressive stress in both deposition methods, with a significant reduction at higher process temperatures. We also analyzed the transport mechanisms in all films and found that hopping conduction and the Poole-Frenkel satisfactorily describes the current–electric field characteristics at low and intermediate electric fields, while Fowler–Nordheim tunneling dominates at high electric fields. These findings provide valuable insights into optimizing TEOS-based SiO2 films for reliable microelectronic and power device applications.17
3D Multi-GNSS Over-The-Air Wave Field Synthesis Testbed
No full text30973105This paper presents a novel three-dimensional (3D) over-the-air (OTA) wave field synthesis (WFS) testbed designed for global navigation satellite systems (GNSS). Unlike traditional conducted and open-field tests, which are limited in realism, the 3D OTA WFS approach enables the realistic emulation of various GNSS scenarios under controlled and repeatable conditions. This allows for effective comparisons of receivers and algorithms, particularly for those utilizing multi/beamforming antenna technologies. The setup used for these tests features a GNSS simulator, an OTA channel emulator and 16 dual-polarized antennas installed within an anechoic chamber. The validation measurements demonstrate the capability of the system to produce high-quality electromagnetic fields across the upper hemisphere of the chamber. The capability for precise GNSS emulation is validated by comparing results with real-world outdoor measurements using the carrier-to-noise-density ratio (C/N0) metric. The big advantage of the emulation lies in the total control of the scenario, which means that random effects such as multipath propagation and scattering could be neglected in case it is required. However, these effects can also be included in the emulation, as shown in the final results of this contribution. In order to show the versatility of the emulation, the analysis of a GNSS jamming attack is also included to assess the performance of an anti-jamming system. The OTA approach allows for comprehensive testing, addressing issues of antenna gain patterns and signal reflection impacts on performance. The findings illustrate the advantages of the 3D OTA WFS setup for emulating GNSS signals. The results underscore the potential for improved reliability and accuracy in GNSS performance evaluations, thereby advancing research in integrated GNSS technologies
Erratum to "Thermal, rheological, and microstructural characterization of composite gels from fava bean protein and pea starch" [Food Hydrocolloids 172 Part 1 (2026) 111883]
No full textThis study investigated the influence of different ratios of pea starch and fava bean protein on the textural, rheological, and microstructural properties of heat-induced composite gels. Blends with starch-to-protein ratios of 100:0, 75:25, 50:50, 25:75, 0:100 were prepared at three dry matter levels (10, 15, 20%). Substituting starch with protein resulted in decreased peak and final viscosities during the pasting, along with elevated gelatinization temperatures. Differential Scanning Calorimetry revealed that starch gelatinization preceded protein denaturation and confirmed that starch and protein compete for the available water. Rheological measurements showed higher storage (G′) and loss (G″) moduli in starch-rich gels. Increasing protein content led to a weakening of the gel network, as reflected by higher loss tangent (tan δ) values and stronger frequency dependence. Upon exceeding the least gelation concentration of fava bean protein, a stable network was formed. At high starch levels, densely packed starch-rich regions were surrounded by the protein phase. Increasing protein content led to a more homogenous distribution of both components, with no clearly dominant phase. At higher protein levels, a protein-dominated continuous phase was observed. These microstructural transitions closely reflected rheological behavior. The findings emphasize the critical role of the starch-to-protein ratio and the least gelation concentration threshold in tailoring gel texture and structure, providing a foundation for the development of plant-based foods with customizable textural properties.17
V2V4Real: A Real-World Large-Scale Dataset for Vehicle-to-Vehicle Cooperative Perception
No full text1371213722Modern perception systems of autonomous vehicles are known to be sensitive to occlusions and lack the capability of long perceiving range. It has been one of the key bottlenecks that prevents Level 5 autonomy. Recent research has demonstrated that the Vehicle-to-Vehicle (V2V) cooperative perception system has great potential to revolutionize the autonomous driving industry. However, the lack of a real-world dataset hinders the progress of this field. To facilitate the development of cooperative perception, we present V2V4Real, the first large-scale real-world multi-modal dataset for V2V perception. The data is collected by two vehicles equipped with multi-modal sensors driving together through diverse scenarios. Our V2V4Real dataset covers a driving area of 410 km, comprising 20K LiDAR frames, 40K RGB frames, 240K annotated 3D bounding boxes for 5 classes, and HDMaps that cover all the driving routes. V2V4Real introduces three perception tasks, including cooperative 3D object detection, cooperative 3D object tracking, and Sim2Real domain adaptation for cooperative perception. We provide comprehensive benchmarks of recent cooperative perception algorithms on three tasks. The V2V4Real dataset can be found at research.seas.ucla.edu/mobility-lab/v2v4real/
Using Multi-Modal LLMs to Create Models for Fault Diagnosis
No full textCreating models that are usable for fault diagnosis is hard. This is especially true for cyber-physical systems that are subject to architectural changes and may need to be adapted to different product variants intermittently. We therefore can no longer rely on expert-defined and static models for many systems. Instead, models need to be created more cheaply and need to adapt to different circumstances. In this article we present a novel approach to create physical models for process industry systems using multi-modal large language models (i.e ChatGPT). We present a five-step prompting approach that uses a piping and instrumentation diagram (P&ID) and natural language prompts as its input. We show that we are able to generate physical models of three systems of a well-known benchmark. We further show that we are able to diagnose faults for all of these systems by using the Fault Diagnosis Toolbox. We found that while multi-modal large language models (MLLMs) are a promising method for automated model creation, they have significant drawbacks
Experimental Demonstration of High-Dimensional Hyperentagled Quantum States
No full textIn this work we demonstrate experimentally the realization and characterization of high-dimensional hyperentangled quantum states of light in the time-energy and polarization degree of freedom. For this goal we use an ultra-bright Sagnac-type source to generate entangled photon in the telecommunication band by means of spontaneous parametric down-conversion (SPDC). To project the time-energy entanglement to a 4-dimensional space we implemented a 4-arm interferometer, consisting of a cascade array of 2-arm unbalanced Mach-Zehnder interferometers (UMZI). For the case of the projection of the entangled quantum states in the polarization degree of freedom (DoF) a compact, stable and robust polarization analysis module (PAM) was implemented to project polarization correlations in two non-orthogonal basis
Application of MXene-based nanocomposites in electrochemical biosensors
No full textMXene is among the most attractive two-dimensional (2D) nanomaterials for construction of advanced electrochemical biosensors owing to their superior electrical and mechanical properties, tunable hydrophilicity, large surface-to-volume ratio, excellent electrochemical properties, facile functionalization, and good biocompatibility. Furthermore, MXene nanomaterials possess excellent conductivity, facilitating the electrical signal transmission in biosensors. They also offer a large surface area, providing active sites for interactions with biomolecules, thereby enhanced detection of various analytes contributing to the sensitive and precise construction of biosensors. In this review, recent breakthroughs in the concept, development, and bio-sensing applications of various MXene-based electrochemical biosensors have been summarized. More specifically, this review attempts to detail the MXene nanocomposites and MXene hydrogels as the most effective strategies to make full use of the electrochemical performance of MXene as well as the reports where MXene nanocomposites have been used in electrochemical biosensors, so that the future prospect of these cutting-edge 2D materials can be analyzed. This will help in developing innovative MXene-based biosensing platforms towards advancing the application of such devices in point-of-care (POC) diagnosis.3