Search Results (14,289)

The transient mechanism of dual-boundary dynamic opening in the inlet during stage transition of an integral solid rocket ramjet (ISRR) remains insufficiently understood. To address this issue, a combined approach involving numerical simulations and free-jet experiments was employed. A parametric model describing the time-sequenced opening of inlet and outlet cover was established. The influences of sequence and progression of opening and flight conditions on transient flow evolution and inlet stability were systematically examined. It is found that when the inlet is opened first, a “dead cavity” tends to form inside the inlet, which subsequently triggers pronounced pressure oscillations. Under baseline conditions, the peak outlet pressure reaches approximately 0.90 MPa, with a dominant frequency of about 66.7 Hz. Conversely, when the outlet is opened first, the cavity-induced oscillation is effectively suppressed; however, a transient “flow choking” overpressure and a delayed establishment of the flow field are observed. The discrepancies between simulations and experiments for key pressure characteristics under two representative opening modes are maintained within 5%, confirming the robustness of the proposed methodology. Further analysis reveals that increasing the Mach number markedly intensifies flow instability and reduces the stability margin, whereas higher flight altitudes help attenuate cavity oscillations. A strong coupling between the opening rate and temporal sequence is also identified. Specifically, for inlet-first scenarios, a slower inlet opening combined with a rapid outlet opening is preferable, while for outlet-first cases, rapid opening on both sides yields better performance. On this basis, a “stability window map” defined by the temporal difference (Δt) and opening duration (Topen) is proposed. This map delineates the distributions of stable, transitional, and hazardous regimes under varying conditions, which may offer a quantitative reference for adaptive control strategies in the ISRR stage of transition. Interestingly, these findings suggest that slight timing adjustments could substantially reshape the transient flow behavior. Notably, the introduction of the dual-boundary temporally coordinated forcing leads to flow responses within the inlet that exhibits pronounced path dependence and non-uniqueness. Such behavior deviates from the conventional understanding established under the single-boundary frameworks, where transient mode-transition processes were typically assumed to be uniquely determined. More importantly, these findings offer a renewed physical interpretation of inlet mode-transition dynamics, thereby providing both quantitative support and practical guidance for the adaptive design of ISRR transition control strategies. In particular, the results suggest that incorporating multi-boundary temporal effects could significantly enhance the robustness and flexibility of the control-law formulation. Full article

Inverse modeling for subsurface flow represents a fundamental scientific challenge in hydrogeology and geotechnical engineering, which seeks to reconstruct critical hydrogeological parameters from sparse observational constraints. The marked spatial heterogeneity of subsurface formations, combined with the prohibitively high costs of data acquisition, renders parameter inversion, especially with very sparse supervision, inherently ill-posed and susceptible to non-uniqueness and instability. Numerical simulation-based iterative inversion methods are computationally expensive and time-consuming. Purely data-driven approaches require extensive labeled data, whereas the existing physics-informed methods lack an explicit architecture-level information transfer channel between parameter and response fields. Under sparse supervision, this prevents hydraulic head observations from effectively constraining hydraulic conductivity identification, resulting in weak parameter identifiability. In this work, we propose a physics-coupled and message-transferred inverse modeling method for transient subsurface flow problems with very sparse supervision. Specifically, the static parameter field estimated by the inversion network is explicitly incorporated into the dynamic response prediction network, and the static inversion and dynamic prediction networks are physics-coupled by the governing equations in parallel. This method enables accurate hydraulic conductivity inversion under extremely limited supervision. Experiments on multiple parameter fields, label scales, and noise levels demonstrate accurate and stable inversion performance under very sparse supervision, with ensemble-based uncertainty analysis, further confirming the reliability of the proposed method. Full article

This manuscript investigates conformal $\eta$-Ricci–Yamabe solitons of type $(\kappa ,l)$ on Kählerian Norden space-time admitting a Kählerian Norden torse-forming vector field. Necessary conditions are obtained under which the soliton exhibits expanding, steady, or shrinking behavior. The analysis is further extended to several physically relevant fluid models, including dark fluid, dust fluid, stiff matter, and radiational fluid, and the corresponding geometric constraints are derived. In addition, structural results are established for Kählerian Norden space-times with a vanishing space–matter tensor and with a divergence-free matter tensor, highlighting their influence on the curvature geometry. The study also addresses several intrinsic curvature conditions of the space-time, such as conformal flatness, Ricci semi-symmetry, Ricci recurrence, and pseudo-Ricci symmetry, leading to a collection of geometric and physical characterizations. The results obtained provide a unified geometric framework linking Ricci–Yamabe soliton structures, fluid dynamics, and curvature properties within the setting of Kählerian Norden geometry. Full article

Ablation and harvesting are among the most labor-intensive and physically demanding operations in oil palm cultivation, often resulting in significant drudgery and safety concerns when performed manually through climbing or pole-assisted methods. To overcome these challenges, a motorized backpack-type machine was developed and evaluated for its field performance, ergonomics, and economic feasibility. The machine met required quality standards and exhibited satisfactory performance under field conditions, achieving average ablation and harvesting capacities of 286 inflorescences per day and 4.115 t day⁻¹, with actual field capacities of 0.727 ha h⁻¹ (ablation), 0.516 ha h⁻¹ (sickle), and 0.537 ha h⁻¹ (chisel), and field efficiencies of 81.23%, 76.3%, and 79.91%, respectively. Ergonomic evaluation indicated that operation of the machine falls within a moderate workload category, thereby reducing operator fatigue compared to manual methods. Economic analysis further revealed that the cost of operation was substantially reduced to 3.02 USD t⁻¹ and 60.40 USD ha⁻¹ year⁻¹, resulting in increased harvester earnings of 174.72% and 64.83% compared to climbing and pole harvesting methods, respectively. These findings demonstrate that the motorized backpack machine is a practical, efficient, and economically viable alternative to traditional techniques and minimizes drudgery while improving productivity and profitability in oil palm plantations. Full article

This Special Issue (SI) was intended to gather high-quality original research articles and reviews on the above topic, with an emphasis on the essential role of VLF (very low frequency, 3–30 kHz)/ELF (extremely low frequency, 1 Hz–3 kHz) wave phenomena in a wide range of scientific fields from astrophysics, space physics, ionospheric physics, atmospheric electricity, and seismo-electromagnetics [...] Full article

Magnetizing living cells with superparamagnetic iron oxide nanoparticles (SPIONs) enables their remote manipulation using external magnetic field. This lays the foundation for magnetically assembling tissue precursors within cell-friendly, proliferation-permissive environments and holds considerable promise for biomedical applications, particularly in the development of complex single- and multicellular tissue constructs for bone and organ reconstruction. However, progress in this field is limited by the lack of robust mathematical tools for accurate control of ensembles of magnetic nano- and micro-objects. In practical printing scenarios, collective behavior and unavoidable statistical heterogeneity—such as variations in SPION size and shape or deviations in cell magnetization—render traditional equation-based modeling inadequate. We developed a hybrid modeling framework integrating conventional physics-based simulations with artificial intelligence-driven image analysis. Dynamic parameters were extracted from video recordings of magnetized cells moving within model microfluidic devices exposed to well-defined magnetic fields and gradients. The AI-based analysis enabled quantitative characterization of ensemble behavior under heterogeneous conditions. The proposed framework successfully captured the collective dynamics of magnetized cell ensembles and enabled accurate control of their spatial organization under external magnetic actuation. The integration of simulation and data-driven analysis provided robust parameter identification despite statistical heterogeneity within the system. This integrated modeling approach provides a practical and effective tool for controlling the three-dimensional magnetic assembly of living cells, with strong potential for applications in tissue engineering. Full article

In the field of subgrade compaction quality assessment, accurate volume measurement of excavated pits is hindered by non-uniform point cloud distribution, environmental noise interference, and complex irregular boundary features. To address these challenges, this paper proposes a robust volume detection framework that integrates adaptive point cloud refinement and morphological discrimination. First, a pose normalization method employing RANSAC plane fitting and rigid body transformation corrects the spatial orientation of the raw point clouds. To balance data redundancy removal with feature preservation, a gradient adaptive simplification strategy based on local density feedback and K-nearest neighbor estimation is developed. Subsequently, a cross-sectional area calculation model utilizing piecewise-cubic polynomial fitting is proposed to mitigate boundary noise and accurately reconstruct irregular contours. Furthermore, a dynamic outlier removal mechanism based on the Median Absolute Deviation (MAD) and sliding windows is introduced to eliminate non-physical geometric fluctuations. Finally, the total volume is aggregated using a hybrid strategy of Simpson’s rule and a frustum compensation operator. Experimental results on simulated pits with typical topological defects demonstrate that the proposed algorithm outperforms traditional methods, achieving an average relative volume error of less than 0.8%. This approach significantly improves the robustness and precision of sensor-based automated subgrade compaction quality measurement. Full article

For low-energy rare-isotope beam experiments, a large-area quadrant silicon pad sensor (5 × 5 cm${}^2$) has been developed for the TexAT active target system. Unlike finely segmented sensors such as small-scale pad or strip sensors, the operational stability of large-area segmented sensors is critically dependent on the electric field distribution at the device termination; thus, optimizing the guard-ring design is essential to prevent premature breakdown. In this study, we systematically investigated three different guard-ring configurations featuring 6, 9, and 14 rings (denoted as G6, G9, and G14, respectively) through TCAD simulations and experimental measurements. The TCAD results demonstrated that the G9 design, which utilizes a graded-spacing strategy, is more effective in mitigating the maximum electric-field concentration at the sensor edge than designs that simply feature a higher number of rings (G14). Accordingly, the G9-based quadrant sensor was fabricated, and its performance was validated through electrical performance evaluations and radioactive source tests, confirming a low leakage current of several tens of nA and an energy resolution of approximately 31 keV (FWHM) (for 3.18 MeV $\alpha$-particles from ${}^{148}$Gd). Furthermore, beam tests performed at the RAON facility verified the operational reliability of the sensor in a practical in-beam environment. In conclusion, these results provide essential design criteria for large-area silicon detectors in rare-isotope beam experiments, and the developed detectors will be equipped to the TexAT array to enhance the precision of nuclear physics measurements. Full article

Transient analyses of liquid-fueled molten salt reactors involve strong coupling among neutronics, delayed neutron precursor transport, thermal–hydraulics, and solid heat transfer, leading to high computational costs for repeated high-fidelity simulations. To enable fast multi-physics prediction at unseen operating conditions, a parametric non-intrusive reduced-order model (ROM) combining proper orthogonal decomposition (POD) and higher-order dynamic mode decomposition (HODMD) is developed. Coupled full-order snapshots generated from an OpenFOAM-based one-eighth symmetric core model based on a simplified MSRE benchmark configuration are used to construct reduced representations for 11 physical fields. The POD truncation rank, HODMD delay dimension, and interpolation model are selected using leave-one-out cross-validation, with polynomial, radial basis function, and Gaussian process regression models considered as interpolation candidates. For unseen parameter points, the model maintains high accuracy in both the interpolation stage and the temporal extrapolation stage. In the temporal extrapolation stage, the highest mean relative L₂ error for the inlet-temperature-step case is 2.112%, whereas all mean relative L₂ errors for the inlet-velocity-step case remain below 0.177%. The results indicate that, under the present cases and parameter settings, the proposed framework provides an accurate and rapid surrogate for multi-physics transient prediction. Full article

Series arc faults on the DC side of photovoltaic (PV) systems are a critical hazard that can trigger system fires. Conventional contact-based detection methods suffer from cumbersome installation and high retrofit cost, whereas existing non-contact approaches mostly rely on megahertz-level high-frequency sampling and therefore require expensive radio-frequency instrumentation or high-performance computing platforms. As a result, it remains difficult to simultaneously achieve strong interference immunity and real-time performance on low-cost embedded devices with limited resources. To address this engineering paradox between high-frequency sampling and constrained computational capability, this paper proposes a fully embedded, non-contact arc fault detection system based on a 12–80 kHz low-frequency sub-band selection strategy. By exploiting the physical characteristic of broadband energy elevation induced by arc faults, the proposed strategy avoids dependence on high-bandwidth hardware. Guided by this strategy, a Moebius-topology coaxial shielded loop antenna is employed as the near-field sensor, while an ultra-simplified passive analog front end is constructed directly by using the on-chip programmable gain amplifier and analog-to-digital converter of the microcontroller unit, enabling efficient signal acquisition and fast Fourier transform processing within the target sub-band. To cope with complex background noise in the low-frequency range, an environment-adaptive baseline mechanism based on exponential moving average and exponential absolute deviation is developed for dynamic decoupling. In addition, a lightweight INT8-quantized multilayer perceptron is introduced as a nonlinear auxiliary module, thereby forming a robust hybrid decision architecture with complementary rule-based and artificial intelligence components. Experimental results show that, under the tested household, laboratory, and PV-site conditions, the proposed system achieved an overall detection rate of 97%, while the remaining 3% mainly corresponded to failed ignition or non-sustained arc attempts rather than persistent false triggering during normal monitoring. Full article

This study conducted a comprehensive comparative assessment of the effects of site-specific tillage (SST) and uniform-depth tillage (UDT) on soil physical properties, fuel consumption and CO₂ emissions. The aim was to determine whether using different tillage depths based on variability in soil properties associated with apparent electrical conductivity (ECa) could improve the efficiency of soil management, which would be beneficial for the soil and the environment. Field experiments were conducted using a multifunctional cultivator with three SST depths (10, 14 and 18 cm), which were distributed over variable soil management zones. UDT was applied at a constant depth of 15 cm. The results of the experimental studies showed that SST affected the physical properties of the soil in different management zones with different tillage depths. Reduced tillage depths ensured adequate soil physical properties in areas of lower soil resistance, while deeper tillage was only effective in areas of higher soil resistance. Soil density in the top 0–10 cm soil layer varied within the plant-friendly range of 1.2–1.3 g cm⁻¹ in the region and 1.4–1.5 g cm⁻¹ in the deeper 10–20 cm layer, while total soil porosity responses differed in different management zones. UDT reduced total soil porosity by 3.17% and 3.5% in the top and deeper soil layers, respectively. Changes in total soil porosity due to SST in the 0–10 cm layer depended on tillage depth: it decreased slightly at 10 cm, remained unchanged at 14 cm and increased slightly at 18 cm. In addition, SST reduced fuel consumption and associated CO₂ emissions compared with UDT, with environmental impact related to fuel combustion decreasing by approximately 14%. These findings demonstrate that site-specific tillage, when guided by soil variability, can improve the efficiency and environmental sustainability of tillage operations without compromising soil physical properties. Full article

Geothermal energy extraction, hydrocarbon recovery, and CO₂ geological sequestration are frequently hindered by interfacial barriers and slow mass transfer. While high-power ultrasound offers a sustainable, purely physical method for reservoir stimulation, its field effectiveness remains debated because traditional macroscopic experiments fail to isolate mechanisms like acoustic streaming and cavitation. This review systematically examines acoustic mechanisms in porous media via microfluidic visualization, focusing on pore-scale fluid dynamics during enhanced oil recovery, hydrate dissociation, and CO₂ sequestration. Microscopic evidence reveals that fluid transport mechanisms depend heavily on pore geometry and local acoustic intensity. In wider channels, nonlinear acoustic flow provides sustained, directed convection to strip away concentration boundary layers; in narrow throats, microjets and pulsed stresses generated by transient cavitation are responsible for physically breaking capillary barriers. The spatiotemporal synergy of these mechanisms is critical for multiphase fluid transport in tight porous networks. Pore geometry serves not only as the application context but also as a core physical variable. To translate microfluidic results into reservoir-scale applications, future research must address two-dimensional simplifications, thermodynamic discrepancies under high-temperature and high-pressure conditions, and bubble cluster interactions, alongside the development of adaptive frequency-modulated control and multiscale computational models. Full article

Mechanized harvesting in the industrial tomato sector is currently bottlenecked by excessive mechanical injuries and elevated levels of foreign materials generated during electro-mechanical combine harvesting operations. To combat these limitations, this comprehensive review explores recent breakthroughs in harvester-mounted smart grading systems engineered specifically for complex, open-field conditions. Rather than relying solely on conventional optical inspection, the study examines the transition toward advanced, heterogeneous edge-computing frameworks—incorporating FPGAs and embedded GPUs—deployed within electro-mechanical harvesting platforms. This architectural evolution plays a crucial role in mitigating unpredictable processing delays caused by intense operational vibrations, although achieving absolute real-time stability under extreme field conditions remains an ongoing challenge. To minimize bruising and physical deterioration, our analysis synthesizes findings from multi-scale explicit dynamic finite element simulations, unpacking the underlying microstructural failure modes of the crop. We illustrate how regulating applied forces via soft robotic effectors can help approach a ‘damage-free’ handling threshold, though empirical results vary depending on fruit maturity and dynamic operational speeds. Furthermore, coupling multi-modal sensor fusion with Convolutional Neural Networks (CNNs) shows promising potential for non-destructive internal property evaluation under the vibration, dust, and throughput constraints of electro-mechanical harvesters, pending broader validation across diverse field datasets. Ultimately, by projecting future trends in onboard electro-mechanical harvester separation and advocating for a closer synergy between agronomic practices and machine engineering, this paper delivers a comprehensive blueprint for building next-generation, highly resilient, and gentle sorting machinery. Full article

In the context of the increasing demands of precision manufacturing and nanotechnology, especially for emerging fields such as Oxide oxide films in Nuclear nuclear fuel assemblies, the measurement of multi-layer inhomogeneous thin films faces significant challenges. Traditional spectroscopic interference thickness measurement techniques have limitations in handling dispersion interference, parameter coupling, and the efficient solution of nonlinear inverse problems. This study proposes a new model that integrates deep learning and physical model fitting. It constructs a theoretical model of multi-layer thin-film interference spectroscopy based on the Lorentz–Drude formula, uses a generative adversarial network (GAN) for initial structure analysis, and builds a two-layer optimization framework of “deep learning rough positioning—physical model fine fitting”. The research aims to break through the limitations of traditional methods, improve measurement accuracy and anti-noise ability, and provide a key technical support for emerging fields. Full article

Background: Optically pumped magnetometers (OPMs) have emerged as a promising technology for neuromagnetic recording in humans. Current state-of-the-art OPM systems are housed in large magnetically shielded rooms to reduce external electromagnetic noise and typically comprise sensor arrays covering the entire head. Such systems are extremely costly to purchase and install, and take up large amounts of physical space, which limits the accessibility of this technology to research groups with limited funding. Here we sought to evaluate the utility of a more accessible “starter” OPM system comprising a small cylindrical mu-metal shield and partial sensor coverage. Methods: Twelve participants underwent right-sided median nerve stimulation (MNS) intended to elicit ubiquitous sensorimotor responses: somatosensory-evoked fields (SEFs, comprising N20m, P35m and P60m components) and event-related (de)synchronization (ERD/ERS) of oscillatory neuronal rhythms in the mu and beta frequency ranges. Results: Following MNS, we observed robust N20m and P60m peaks, as well as the expected mu ERD and beta ERS effects. Moreover, these responses could be localized to expected cortical generators. However, we observed markedly lower SNR than that seen in state-of-the-art systems. We make recommendations for further improvements to this system and others like it. Full article