Search Results (1,129)

To improve the efficiency and stability of the system, this paper proposes a monolithic integrated optical path design for a cavity optomechanical accelerometer based on a 250 nm top silicon thickness silicon-on-insulator (SOI) wafer instead of readout through U-shape fiber coupling. Finite Element Analysis (FEA) and Finite-Difference Time-Domain (FDTD) methods are employed to systematically investigate the performance of key optical structures, including the resonant modes and bandgap characteristics of photonic crystal (PhC) microcavities, transmission loss of strip waveguides, coupling efficiency of tapered-lensed fiber-to-waveguide end-faces, coupling characteristics between strip waveguides and PhC waveguides, and the coupling mechanism between PhC waveguides and microcavities. Simulation results demonstrate that the designed PhC microcavity achieves a quality factor (Q-factor) of 2.26 × 10⁵ at a 1550 nm wavelength while the optimized strip waveguide exhibits a low loss of merely 0.2 dB over a 5000 μm transmission length. The strip waveguide to PhC waveguide coupling achieves 92% transmittance at the resonant frequency, corresponding to a loss below 0.4 dB. The optimized edge coupling structure exhibits a transmittance of 75.8% (loss < 1.2 dB), with a 30 μm coupling length scheme (60% transmittance, ~2.2 dB loss) ultimately selected based on process feasibility trade-offs. The total optical path system loss (input to output) is 5.4 dB. The paper confirms that the PhC waveguide–microcavity evanescent coupling method can effectively excite the target cavity mode, ensuring optomechanical coupling efficiency for the accelerometer. This research provides theoretical foundations and design guidelines for the fabrication of high-precision monolithic integrated cavity optomechanical accelerometers. Full article

Semantic segmentation is one of the key tasks in the intelligent interpretation of remote sensing images with extensive potential applications. However, when ultra-high resolution (UHR) remote sensing images exhibit complex background intersections and significant variations in object sizes, existing multimodal fusion segmentation methods based on convolutional neural networks and Transformers face challenges such as limited receptive fields and high secondary complexity, leading to inadequate global context modeling and multimodal feature representation. Moreover, the lack of accurate boundary detail feature constraints in the final segmentation further limits segmentation accuracy. To address these challenges, we propose a novel boundary-enhanced multilevel multimodal fusion Mamba-Large Strip Convolution network (FMLSNet) for remote sensing image segmentation, which offers the advantages of a global receptive field and efficient linear complexity. Specifically, this paper introduces a new multistage Mamba multimodal fusion framework (FMB) for UHR remote sensing image segmentation. By employing an innovative multimodal scanning mechanism integrated with disentanglement strategies to deepen the fusion process, FMB promotes deep fusion of multimodal features and captures cross-modal contextual information at multiple levels, enabling robust and comprehensive feature integration with enriched global semantic context. Additionally, we propose a Large Strip Spatial Detail (LSSD) extraction module, which adaptively combines multi-directional large strip convolutions to capture more precise and fine-grained boundary features. This enables the network to learn detailed spatial features from shallow layers. A large number of experimental results on challenging remote sensing image datasets show that our method exhibits superior performance over state-of-the-art models. Full article

As Chinese open-pit mines progressively transition to deeper operations, challenges such as rising stripping ratios, declining slope stability, and environmental degradation have become increasingly pronounced. The sustainability of traditional open-pit mining models faces substantial challenges. Underground mining, offering higher resource recovery rates and minimal environmental disruption, is emerging as a pivotal technological pathway for the green transformation of mining. Consequently, the transition from open-pit to underground mining has emerged as a central research focus within mining engineering. This paper provides a comprehensive review of key technological advancements in this transition, emphasizing core issues such as mine development system selection, mining method choices, slope stability control, and crown pillar design. A typical case study of the Anhui Xinqiao Iron Mine is presented to analyze its engineering approaches and practical experiences in joint development, backfilling mining, and ecological restoration. The findings indicate that the mine has achieved multi-objective optimization of resource utilization, environmental coordination, and operational capacity while ensuring safety and recovery efficiency. This offers a replicable and scalable technological demonstration for the green transformation of similar mines around the world. Full article

Effective real-time control systems require fast and accurate models. The roll bending models presented in this paper are proposed for a real-time control system for the design of the rolling schedule. The roll bending, with other factors, defines the shape of the roll surface, its convexity, and finally the shape of the final product of the flat rolling, its convexity, and its flatness. This paper presents accurate finite element (FE) models for a four-high mill. The models serve to obtain accurate solutions to the problem of roll bending, taking into account the rolling force, width of the rolling sheet (strip), initial shape of the roll surface, and the anti-bending force. The results of the FE simulation are used to train three models developed on the basis of the neural network (NN) for the solution of one direct and two inverse tasks. The pre-trained NN model gives accurate results and is faster than the FE model (FEM). The calculation time on a personal computer for one case of 3D FEM is 1 to 2 min, for 2D FEM it is 1 s, and for NN it is less than 1 ms. The results can be immediately used by other models of the real-time control system. A novelty of the research presented in the paper is the creation of complex applications of the FE method and an NN as a reduced-order model (ROM) for prediction of roll bending and calculation of sheet (strip) convexity, rolling, and anti-bending forces to obtain the required convexity. Full article

Underwater towed systems (UTSs) are widely used in underwater exploration and oceanographic data acquisition. However, the heave motion information of the towing ship is usually affected by the measurement transmitting delay, sensor noise and surface waves, which will result in uncontrolled depth variation of the towed vehicle, so as to adversely affect the monitoring performance and mechanical robustness of the UTS. To resolve this problem, a heave motion prediction approach based on sparse Bayesian learning (SBL) incorporated with empirical mode decomposition (EMD) for the UTS is proposed in this paper. With the proposed approach, a heave motion model of the towing ship with random waves is firstly developed based on strip theory. Meanwhile, the EMD is employed to eliminate the high-frequency noise of the measurement data to restore low-frequency towing ship motion. And then, the SBL is utilized to train the weight parameters in the built model to predict the heave motion, which not only reconstruct the heave motion from non-stationary sensor signals with noise but also prevent overfitting. Furthermore, the depth compensation of the towed vehicle is then performed using the predicted heave motion. Finally, experimental results demonstrate that the proposed EMD-SBL method significantly improves both the prediction accuracy and model adaptability under various sea conditions, and it also guarantees that the maximum prediction depth error of the heave motion does not exceed 1 cm. Full article

Continuous SiC fiber-reinforced SiC matrix composites (SiC/SiC), as structural heat protection integrated materials, are often used in parts for large-area heat protection and sharp leading edges, and there are a variety of low-velocity impact events in their service. In this paper, a drop hammer impact test was conducted using narrow strip samples to simulate the low-velocity impact damage process of sharp-edged components. During the test, different impact energies and impact times were set to focus on investigating the low-velocity impact damage characteristics of 2.5D SiC/SiC composites. To further analyze the damage mechanism, computed tomography (CT) was used to observe the crack propagation paths and distribution states of the composites before and after impact, while scanning electron microscopy (SEM) was employed to characterize the differences in the micro-morphology of their fracture surfaces. The results show that the in-plane impact behavior of a 2.5D needled SiC/SiC composite strip samples differs from the conventional three-stage pattern. In addition to the three stages observed in the energy–time curve—namely in the quasi-linear elastic region, the severe load drop region, and the rebound stage after peak impact energy—a plateau stage appears when the impact energy is 1 J. During the impact process, interlayer load transfer is achieved through the connection of needled fibers, which continuously provide significant structural support, with obvious fiber pull-out and debonding phenomena. When the samples are subjected to two impacts, damage accumulation occurs inside the material. Under conditions with the same total energy, multiple impacts cause more severe damage to the material compared to a single impact. Full article

Aircraft, as electromagnetically complex targets, have radar cross-sections (RCSs) that are influenced by various factors, with the inlet duct being a critical component that often serves as a primary source of electromagnetic scattering, significantly impacting the scattering characteristics. In light of the conflict between aerodynamic performance and electromagnetic characteristics in the design of aircraft engine inlet grilles, this paper proposes a metasurface radar-absorbing inlet grille (RIG) solution based on a NACA symmetric airfoil. The RIG adopts a sandwich structure consisting of a polyethylene terephthalate (PET) dielectric substrate, a copper zigzag metal strip array, and an indium tin oxide (ITO) resistive film. By leveraging the principles of surface plasmon polaritons, electromagnetic wave absorption can be achieved. To enhance the design efficiency, a multi-objective Bayesian optimization framework driven by the expected hypervolume improvement (EHVI) is constructed. The results show that, compared with a conventional rectangular cross-section grille, an airfoil-shaped grille under the same constraints will reduce both aerodynamic losses and the absorption bandwidth. After 100-step EHVI–Bayesian optimization, the optimized balanced model attains a 57.79% reduction in aerodynamic loss relative to the rectangular-shaped grille, while its absorption bandwidth increases by 111.99%. The RCS exhibits a reduction of over 8.77 dBsm in the high-frequency band. These results confirm that the proposed optimization design process can effectively balance the conflict between aerodynamic performance and stealth performance for RIGs, reducing the signal strength of aircraft engine inlets. Full article

This paper assesses the complexity and diversity of vegetation in grassy strips with spontaneous plants between tree rows in three fruit orchards (plum, cherry, apple) in Dolj County, Romania, using structural and biodiversity indices. It addresses the lack of data on spontaneous vegetation in Romanian orchards, supporting improved plantation management and native biodiversity conservation. The study found that grassy strips supported high wild herbaceous diversity and a complex, heterogeneous ecological structure, with the apple orchard showing the highest biodiversity. Species diversity, evaluated through species richness, evenness, and diversity indices (Shannon, Simpson, Menhinick, Gleason, etc.), showed species richness ranging from 30 species in the cherry orchard to 40 in the apple orchard. Several species, including Capsella bursa-pastoris, Geranium pusillum, Poa pratensis, Veronica hederifolia, Lolium perenne, and Convolvulus arvensis, were present in 100% of samples, making them constant species from a phytosociological perspective. Their presence indicates relatively stable plant communities in each orchard. From a phytocoenological view, an ecological plant community is defined not only by species composition but also by constancy and co-occurrence in sampling units. Dominance remained low in all orchards, indicating no single plant dominated, while evenness showed a uniform distribution of species. Full article

Policies and technical safeguards for artificial intelligence (AI) governance have implicitly assumed that AI systems will continue to operate via massive power-hungry data centers operated by large companies like Google and OpenAI. However, the present cloud-based AI paradigm is being challenged by rapidly advancing software and hardware technologies. Open-source AI models now run on personal computers and devices, invisible to regulators and stripped of safety constraints. The capabilities of local-scale AI models now lag just months behind those of state-of-the-art proprietary models. Wider adoption of local AI promises significant benefits, such as ensuring privacy and autonomy. However, adopting local AI also threatens to undermine the current approach to AI safety. In this paper, we review how technical safeguards fail when users control the code, and regulatory frameworks cannot address decentralized systems as deployment becomes invisible. We further propose ways to harness local AI’s democratizing potential while managing its risks, aimed at guiding responsible technical development and informing community-led policy: (1) adapting technical safeguards for local AI, including content provenance tracking, configurable safe computing environments, and distributed open-source oversight; and (2) shaping AI policy for a decentralized ecosystem, including polycentric governance mechanisms, integrating community participation, and tailored safe harbors for liability. Full article

Cu-Ni-Si is a prominent example of a high-end lead frame copper alloy. The enhancement of strength without compromising electrical conductivity has emerged as a prominent research focus. The evolution of the precipitates exerts a significant influence on the strength and electrical conductivity of Cu-Ni-Si-Co-Mg alloys. In this paper, the effects of aging treatment and Mg addition on the properties and precipitates of cold-rolled Cu-Ni-Si-Co alloys were studied. The precipitate was (Ni, Co)₂Si and was in a strip shape. During aging, precipitation and coarsening of the (Ni, Co)₂Si precipitates were observed. In the early stage of aging, a significant number of fine (Ni, Co)₂Si precipitates were formed. These fine precipitates could not only have a better effect of precipitation strengthening, but also impeded the dislocation movement, thus increasing the dislocation density and improving the dislocation strengthening effect. However, the coarsening of the precipitates became dominant with increasing aging times. Therefore, the strengthening effect was weakened. The addition of 0.12% Mg promoted finer and more diffuse precipitates, which not only improving the tensile strength by 100–200 MPa, but also exhibiting a smaller effect on the electrical conductivity. However, further increases in Mg contents resulted in a significant decrease in electrical conductivity, with little change in the tensile strength. The optimum amount of added Mg was 0.12%, and the aging parameters were 300 °C and 20 min. Full article

This paper deals with “Zn-19Al-6Mg” coatings and their solidification structure is the basis for the study of the alloy’s properties. The solidification equilibrium phase diagram of this alloy was calculated using thermodynamic software. Samples were taken from the billets of this alloy for differential thermal analysis experiments. By combining the phase diagram and the experimental results of differential thermal analysis, the solidification structure of the Zn-19Al-6Mg alloy was obtained. The phases in the solidified structure were identified by means of SEM, EDS, XRD, etc. The research finds that the solidification structure of the Zn-19Al-6Mg alloy is composed of the β-Al phase, the α-Al phase, the MgZn₂ phase, and the Mg₂Zn₁₁ phase. During the actual solidification process of the alloy, due to the large cooling rate, Zn-rich phases will appear in the microstructure. The research results provide a basis for the regulation of the coating structure when preparing Zn-19Al-6Mg-coated sheets and strips. Full article

One of the important parameters of the sheet metal forming process is the coefficient of friction (CoF). Therefore, monitoring the friction coefficient value is essential to ensure product quality, increase productivity, reduce environmental impact, and avoid product defects. Conventional CoF monitoring techniques pose a number of problems, including the difficulty in identifying the features of force signals that are sensitive to the variation in the coefficient of friction. To overcome these difficulties, this paper proposes a new approach to apply unsupervised artificial intelligence techniques with unbalanced data to classify the CoF of DP780 (HCT780X acc. to EN 10346:2015 standard) steel sheets in strip-drawing tests. During sheet metal forming (SMF), the CoF changes owing to the evolution of the contact conditions at the tool–sheet metal interface. The surface topography, the contact loads, and the material behaviour affect the phenomena in the contact zone. Therefore, classification is required to identify possible disturbances in the friction process causing the change in the CoF, based on the analysis of the friction process parameters and the change in the sheet metal’s surface roughness. The Kohonen self-organising map (SOM) was created based on the surface topography parameters collected and used for CoF classification. The CoF determinations were performed in the strip-drawing test under different lubrication conditions, contact pressures, and sliding speeds. The results showed that it is possible to classify the CoF using an SOM for unbalanced data, using only the surface roughness parameter Sq and selected friction test parameters, with a classification accuracy of up to 98%. Full article

The adhesive properties between rubber asphalt mastic and aggregate are crucial to rubber asphalt mixtures’ stability and moisture resistance. This paper employs surface free energy (SFE) theory and molecular dynamics (MD) to examine the bond strength and debonding behavior at the rubber asphalt mastic–aggregate interface. The results showed that the dispersion fraction of RC1.0 was 7.12 mJ/m² higher than that of RA, and the limestone mineral powder improved the adhesion properties of rubberized asphalt to aggregate and the anti-stripping properties. SiO₂ and CaCO₃ are contributors to the van der Waals and electrostatic forces between rubber asphalt–aggregate, respectively. The high concentration of mineral powder has a bridging effect in rubber asphalt mastic–aggregate. CaCO₃ filler is more pronounced in enhancing the adhesion properties of rubber asphalt–aggregate. CaCO₃ mineral powder mainly improves the anti-debonding ability of rubber asphalt–aggregate by reducing the thickness of water film between rubber asphalt–aggregate. Full article

A biharmonic boundary value problem with a singularity is one of the mathematical models of processes in fracture mechanics. It is necessary to have estimates of the function norms in the neighborhood of the singularity point to study the existence and uniqueness of the $R_{\nu }$-generalized solution, its coercive and differential properties of biharmonic boundary value problems with a corner singularity. This paper establishes estimates of a function in the neighborhood of a singularity point in the norms of weighted Lebesgue spaces through its norms in weighted Sobolev spaces over the entire domain, with a minimum weight exponent. In addition, we obtain an estimate of the function norm in a boundary strip for the degeneration of a function on the entire boundary of the domain. These estimates will be useful not only for studying differential problems with singularity, but also in estimating the convergence rate of an approximate solution to an exact one in the weighted finite element method. Full article

The increasing prevalence of sedentary lifestyles poses significant global health challenges, including obesity, diabetes, musculoskeletal disorders, and cardiovascular issues. This paper presents the design and development of a universal smart chair system aimed at mitigating the adverse effects of prolonged sitting. The proposed solution integrates a pressure sensor, vibration motors, an LED strip, and Bluetooth Low-Energy (BLE) communication into a modular and adaptable design. Powered by an STM32WB55CGU6 microcontroller and a rechargeable lithium-ion battery system, the smart chair monitors sitting duration and the user’s posture, and provides alerts through tactile, visual, and auditory notifications. A complementary mobile application allows users to customize sitting time thresholds, monitor activity, and assess battery status. Designed for universal compatibility, the system can be adapted to various chair types. Technical and functional testing demonstrated reliable performance, with the chair operating for over eight workdays on a single charge. The smart chair offers an innovative, cost-effective approach to improving workplace ergonomics and health outcomes, with potential for further enhancements such as posture monitoring. A pilot study with 83 students at VILNIUS TECH showed that the smart chair detected correct posture with 94.78% accuracy, and 97.59% of users responded to alerts by adjusting their posture within an average of 3.27 s. Full article