Search Results (7,123)

This study systematically investigates the high-velocity impact response and energy absorption characteristics of carbon fiber-reinforced plastic (CFRP)—aluminum foam (AlF) hybrid composite structures, aiming to address the growing demand for lightweight yet high-performance energy-absorbing materials in aerospace and protective engineering applications. Particular emphasis is placed on elucidating the influence of key geometric and material parameters, including the aspect ratio of the columns and the relative density of the AlF core. Experimental characterization was first performed using a split Hopkinson pressure bar (SHPB) apparatus to evaluate the dynamic compressive behavior of AlF specimens with four different relative densities (i.e., 0.163, 0.245, 0.374, and 0.437). A finite element (FE) model was then developed and rigorously validated against the experimental data, demonstrating excellent agreement in terms of deformation modes and force–displacement responses. Extensive parametric studies based on the validated FE framework revealed that the proposed CFRP-AlF composite structure achieves a balance between specific energy absorption (SEA) and peak crushing force, showing a significant improvement over conventional CFRP or AlF. The confinement effect of CFRP enables AlF to undergo progressive collapse along designated orientations, thereby endowing the CFRP-AlF composite structure with superior impact resistance. These findings provide critical insight for the design of next-generation lightweight protective structures subjected to extreme dynamic loading conditions. Full article

Given the outstanding magnetic characteristics of lanthanide ions, the development of mononuclear or multinuclear lanthanide complexes becomes imperative. Previous research showed that a series of mononuclear Dy(III) complexes of rhodamine benzoyl hydrazone Schiff base ligands exhibit remarkable single-molecule magnetic properties and fluorescence. In this study, we used analogous ligands to synthesize lanthanide complexes [Dy(HL¹-o)(NO₃)₂(CH₃OH)₂]NO₃·CH₃OH (complex 1·MeOH) and tetranuclear complexes [Ln₄(L¹-c)₂(L²)₂(μ₃-OH)₂(NO₃)₂(CH₃OH)₄](NO₃)₂·2CH₃CN·5CH₃OH·2H₂O (Ln = Dy, complex 2; Ln = Gd, complex 3). Magnetic susceptibility measurements show that 1·2H₂O is a single-molecule magnet, 2 shows slow magnetic relaxation and 3 is a magnetic cooling material with the magnetic entropy change of 9.81 J kg⁻¹ K⁻¹ at 2 K and 5 T. The theoretical calculations on 1·MeOH indicate that it shows good magnetic anisotropy with the calculated energy barrier of 194.6 cm⁻¹. Full article

UAV small target detection in urban security, disaster monitoring, agricultural inspection, and other fields faces the challenge of increasing accuracy and real-time requirements. However, existing detection algorithms still have weak small target representation ability, extensive computational resource overhead, and poor deployment adaptability. Therefore, this paper proposes a lightweight algorithm, I-YOLOv11n, based on YOLOv11n, which is systematically improved in terms of both feature enhancement and structure compression. The RFCBAMConv module that combines deformable convolution and channel–spatial attention is designed to adjust the receptive field and strengthen the edge features dynamically. The multiscale pyramid of STCMSP context and the lightweight Transformer–DyHead hybrid detection head are designed by combining the multiscale hole feature pyramid (DFPC), which realizes the cross-scale semantic modeling and adaptive focusing of the target area. A collaborative lightweight strategy is proposed. Firstly, the semantic discrimination ability of the teacher model for small targets is transferred to guide and protect the subsequent compression process by integrating the mixed knowledge distillation of response alignment, feature imitation, and structure maintenance. Secondly, the LAMP–Taylor channel pruning mechanism is used to compress the model redundancy, mainly to protect the key channels sensitive to shallow small targets. Finally, K-means++ anchor frame optimization based on IoU distance is implemented to adapt the feature structure retained after pruning and the scale distribution of small targets of UAV. While significantly reducing the model size (parameter 3.87 M, calculation 14.7 GFLOPs), the detection accuracy of small targets is effectively maintained and improved. Experiments on VisDrone, AI-TOD, and SODA-A datasets show that the mAP@0.5 and mAP@0.5:0.95 of I-YOLOv11n are 7.1% and 4.9% higher than the benchmark model YOLOv11 n, respectively, while maintaining real-time processing capabilities, verifying its comprehensive advantages in accuracy, light weight, and deployment. Full article

Under the growing demand for processing multimodal and cross-lingual information, traditional retrieval systems have encountered substantial limitations when handling heterogeneous inputs such as images, textual layouts, and multilingual language expressions. To address these challenges, a unified retrieval framework has been proposed, which integrates visual features from images, layout-aware optical character recognition (OCR) text, and bilingual semantic representations in Chinese and English. This framework aims to construct a shared semantic embedding space that mitigates semantic discrepancies across modalities and resolves inconsistencies in cross-lingual mappings. The architecture incorporates three main components: a visual encoder, a structure-aware OCR module, and a multilingual Transformer. Furthermore, a joint contrastive learning loss has been introduced to enhance alignment across both modalities and languages. The proposed method has been evaluated on three core tasks: a single-modality retrieval task from image → OCR, a cross-lingual retrieval task between Chinese and English, and a joint multimodal retrieval task involving image, OCR, and language inputs. Experimental results demonstrate that, in the joint multimodal setting, the proposed model achieved a Precision@10 of 0.693, Recall@10 of 0.684, nDCG@10 of 0.672, and F1@10 of 0.685, substantially outperforming established baselines such as CLIP, LayoutLMv3, and UNITER. Ablation studies revealed that removing either the structure-aware OCR module or the cross-lingual alignment mechanism resulted in a decrease in mean reciprocal rank (MRR) to 0.561, thereby confirming the critical role of these components in reinforcing semantic consistency across modalities. This study highlights the powerful potential of large language models in multimodal semantic fusion and retrieval tasks, providing robust solutions for large-scale semantic understanding and application scenarios in multilingual and multimodal contexts. Full article

To enable multi-channel parallel spectral analysis in array-based devices such as micro-light-emitting diodes (Micro-LEDs) and line-scan spectral confocal systems, the development of compact array spectrometers has become increasingly important. In this work, a novel spectrometer architecture based on a microlens array grating (MLAG) is proposed, which addresses the major limitations of conventional spectrometers, including limited parallel detection capability, bulky structures, and insufficient spatial resolution. By integrating dispersion and focusing within a monolithic device, the system enables simultaneous acquisition across more than 2000 parallel channels within a 10 mm × 10 mm unit consisting of an f = 4 mm microlens and a 600 lines/mm blazed grating. Optimized microlens and aperture alignment allows for flexible control of the divergence angle of the incident light, and the system theoretically achieves nanometer-scale spectral resolution across a 380–780 nm wavelength range, with inter-channel measurement deviation below 1.25%. Experimental results demonstrate that this spectrometer system can theoretically support up to 2070 independently addressable subunits. At a wavelength of 638 nm, the coefficient of variation (CV) of spot spacing among array elements is as low as 1.11%, indicating high uniformity. The spectral repeatability precision is better than 1.0 nm, and after image enhancement, the standard deviation of the diffracted light shift is reduced to just 0.26 nm. The practical spectral resolution achieved is as fine as 3.0 nm. This platform supports wafer-level spectral screening of high-density Micro-LEDs, offering a practical hardware solution for high-precision industrial inline sorting, such as Micro-LED defect inspection. Full article

Thin-film detection technology plays a significant role in particle physics, X-ray imaging and radiation monitoring. In this paper, the detection capability of a Cs₃Cu₂I₅ thin-film scintillator toward α particles is investigated. The flexible thin-film scintillator is fabricated by a facile and cost-effective in situ strategy, exhibiting excellent scintillation properties. Upon α-particle excitation, the light yield of the Cs₃Cu₂I₅ thin-film is 2400 photons/MeV, which greatly benefits its application for single-particle events detection. Moreover, it shows linear energy response within the range of 4.7–5.5 MeV and moderate decay time of 667 ns. We further explored the cryogenic scintillation performance of Cs₃Cu₂I₅@PMMA film. As the temperature decreases from 300 K to 50 K, its light yield gradually increases to 1.3 fold of its original value, while its decay time remains almost unchanged. This scintillator film also shows excellent low-temperature stability and flexible operational stability. This work demonstrates the great potential of the Cs₃Cu₂I₅@PMMA film for the practical utilization in α-particle detection application. Full article

This paper presents a novel EEG-based learning system designed to enhance the efficiency and effectiveness of studying by dynamically adjusting the difficulty level of learning materials based on real-time attention levels. In the training phase, EEG signals corresponding to high and low concentration levels are recorded while participants engage in quizzes to learn and memorize Chinese characters. The attention levels are determined based on performance metrics derived from the quiz results. Following extensive preprocessing, the EEG data undergoes several feature extraction steps: removal of artifacts due to eye blinks and facial movements, segregation of waves based on their frequencies, similarity indexing with respect to delay, binary thresholding, and (PCA). These extracted features are then fed into a k-NN classifier, which accurately distinguishes between high and low attention brain wave patterns, with the labels derived from the quiz performance indicating high or low attention. During the implementation phase, the system continuously monitors the user’s EEG signals while studying. When low attention levels are detected, the system increases the repetition frequency and reduces the difficulty of the flashcards to refocus the user’s attention. Conversely, when high concentration levels are identified, the system escalates the difficulty level of the flashcards to maximize the learning challenge. This adaptive approach ensures a more effective learning experience by maintaining optimal cognitive engagement, resulting in improved learning rates, reduced stress, and increased overall learning efficiency. Our results indicate that this EEG-based adaptive learning system holds significant potential for personalized education, fostering better retention and understanding of Chinese characters. Full article

Polymerase-coupled nanopore sequencing requires DNA polymerases with strong strand displacement activity and high processivity to sustain continuous signal generation. In this study, we characterized two novel B family DNA polymerases, SRHS and BBum, isolated from Bacillus phages SRT01hs and BeachBum, respectively. Both enzymes exhibited robust strand displacement, 3′→5′ exonuclease activity, and maintained processivity under diverse reaction conditions, including across a broad temperature range (10–45 °C) and in the presence of multiple divalent metal cofactors (Mg²⁺, Mn²⁺, Fe²⁺), comparable to the well-characterized Phi29 polymerase. Through biochemical analysis of mutants designed using AlphaFold3-predicted structural models, we identified key residues (G96, M97, D486 in SRHS; S97, M98, A493 in BBum) that modulated exonuclease activity, substrate specificity and metal ion utilization. Engineered variants SRHS_F and BBum_Pro_L efficiently incorporated unnatural nucleotides in the presence of Mg²⁺—a function not observed in Phi29 and other wild-type strand-displacing B family polymerases. These combined biochemical features highlight SRHS and BBum as promising enzymatic scaffolds for nanopore-based long-read sequencing platforms. Full article

To address the critical challenge of synergistically enhancing both high-temperature mechanical properties and thermal conductivity in neutron-absorbing materials for dry storage of spent nuclear fuel, this study proposes an innovative strategy. This approach involves the controlled distribution, size, and crystalline states of nano-Al₂O₃ within an aluminum matrix. By combining plastic deformation and heat treatment, we aim to achieve a structurally integrated functional design. A systematic investigation was conducted on the microstructural evolution of Al₂O₃/10 wt.% B₄C/Al composites in their forged, extruded, and heat-treated states. We also examined how these states affect high-temperature mechanical properties and thermal conductivity. The results indicate that applying hot extrusion deformation along with optimized heat treatment parameters (500 °C for 24 h) allows for a lamellar dispersion of nano-Al₂O₃ and a crystallographic transition from amorphous to γ-phase. As a result, the composite demonstrates a tensile strength of 144 MPa and an enhanced thermal conductivity of 181 W/(m·K) at 350 °C. These findings provide theoretical insights and technical support for ensuring the high density and long-term safety of spent fuel storage materials. Full article

In this work, we introduce a novel method to extract correlations in diagonal quantum states in multi-particle quantum systems, addressing a significant limitation of traditional approaches that require prior knowledge of the density matrices of quantum states. Instead of relying on classical information processing, our method is based on weak couplings and ancillary systems, eliminating the need for classical communication, optimization, and complex calculations. The concept of mutually unbiased bases is intrinsically linked to symmetry, as it entails the uniform distribution of quantum states across distinct bases. Within the framework of our theoretical model, mutually unbiased bases are employed to facilitate weak measurements and to function as the post-selected states. To quantify the correlations in the initial state, we employ the trace distance between the initial state and the product of its marginal states, and illustrate the feasibility and effectiveness of our approach. We generalize the approach to accommodate high-dimensional multi-particle systems for potential applications in quantum information processing and quantum networks. Full article

In this paper, a novel 4H-SiC LDMOS structure with a trench heterojunction in the source (referred as to THD-LDMOS) is proposed and investigated for the first time, to enhance the reverse recovery performance of its parasitic diode. Compared with 4H-SiC, silicon has a smaller band energy, which results in a lower built-in potential for the junction formed by P+ polysilicon and a 4N-SiC N-drift region. A trench P+ polysilicon is introduced in the source side, forming a heterojunction with the N-drift region, and this heterojunction is unipolar and connected in parallel with the body PiN diode. When the LDMOS operates as a freewheeling diode, the trench heterojunction conducts first, preventing the parasitic PiN from turning on and thereby significantly reducing the number of carriers in the N-drift region. Consequently, THD-LDMOS exhibits superior reverse recovery characteristics. The simulation results indicate that the reverse recovery peak current and reverse recovery charge of THD-LDMOS are reduced by 55.5% and 77.6%, respectively, while the other basic electrical characteristics remains unaffected. Full article

Fringe projection profilometry (FPP) has been widely applied in industrial 3D measurement due to its high precision and non-contact advantages. However, FPP often encounters measurement problems with high-dynamic-range objects, consequently impacting phase computation. In this paper, an adaptive exposure time selection method is proposed to calculate the optimal number of exposures and exposure time by using an improved clustering method to divide the region with different reflection degrees. Meanwhile, the phase order sharing strategy is adopted in the phase unwrapping stage, and the same set of complementary Gray code patterns is used to calculate the phase orders under different exposure times. The experimental results demonstrate that the measurement error of the method described in this paper was reduced by 25.4% under almost the same exposure times. Full article

The growth of rural residents’ disposable income is essential for narrowing the income gap between urban and rural areas and promoting integrated development. This study explores how China’s National Main Functional Zoning Plan influences rural household income through its regulatory impact on construction land expansion. Using data from county−level administrative units across China, the research identified the construction land regulation index as a key mediating variable linking zoning policy to changes in household income. By shifting the analytical perspective from a traditional urban–rural classification to a framework aligned with the National Main Functional Zoning Plan, the study reveals how spatial planning tools, particularly differentiated land quota allocations, influence household income. The empirical results confirm a structured causal chain in which zoning policy affects land development intensity, which in turn drives rural income growth. This relationship varies across different functional zones. In key development zones, strict land control limits income potential by constraining land supply. In main agricultural production zones, moderate regulatory control enhances land use efficiency and contributes to higher income levels. In key ecological function zones, ecological constraints require diverse approaches to value realization. The investigation contributes both theoretical and practical insights by elucidating the microeconomic effects of national spatial planning policies and offering actionable guidance for optimizing land use regulation to support income growth tailored to regional functions. Full article

In this paper, we study the classification problem of short-time-window steady-state visual evoked potentials (SSVEPs) and propose a novel deep convolutional network named EEGResNet based on the idea of residual connection to further improve the classification performance. Since the frequency-domain features extracted from short-time-window signals are difficult to distinguish, the EEGResNet starts from the filter bank (FB)-based feature extraction module in the time domain. The FB designed in this paper is composed of four sixth-order Butterworth filters with different bandpass ranges, and the four bandwidths are 19–50 Hz, 14–38 Hz, 9–26 Hz, and 3–14 Hz, respectively. Then, the extracted four feature tensors with the same shape are directly aggregated together. Furthermore, the aggregated features are further learned by a six-layer convolutional neural network with residual connections. Finally, the network output is generated through an adaptive fully connected layer. To prove the effectiveness and superiority of our designed EEGResNet, necessary experiments and comparisons are conducted over two large public datasets. To further verify the application potential of the trained network, a virtual simulation of brain computer interface (BCI) based quadrotor control is presented through V-REP. Full article

Intelligent driving technology is expected to reshape urban transportation, but its promotion is hindered by user acceptance challenges and diverse technical routes. This study proposes a comprehensive user acceptance evaluation framework for intelligent driving from the perspective of value engineering (VE). The novelty of this framework lies in three aspects: (1) It unifies behavioral theory and utility theory under the value engineering framework, and it extracts key indicators such as safety, travel efficiency, trust, comfort, and cost, thus addressing the issue of the lack of integration between subjective and objective factors in previous studies. (2) It establishes a systematic mapping mechanism from technical solutions to evaluation indicators, filling the gap of insufficient targeting at different technical routes in the existing literature. (3) It quantifies acceptance differences via VE’s core formula of V = F/C, overcoming the ambiguity of non-technical evaluation in prior research. A case study comparing single-vehicle intelligence vs. collaborative intelligence and different sensor combinations (vision-only, map fusion, and lidar fusion) shows that collaborative intelligence and vision-based solutions offer higher comprehensive acceptance due to balanced functionality and cost. This framework guides enterprises in technical strategy planning and assists governments in formulating industrial policies by quantifying acceptance differences across technical routes. Full article